Smart Buildings and Technologies for Sustainable Cities in China (Urban Sustainability) 9819963907, 9789819963904

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Urban Sustainability

Tongyu Zhou Yi Chen Wu Deng Ali Cheshmehzangi   Editors

Smart Buildings and Technologies for Sustainable Cities in China

Urban Sustainability Editor-in-Chief Ali Cheshmehzangi , Qingdao City University, Qingdao, Shandong, China

The Urban Sustainability Book Series is a valuable resource for sustainability and urban-related education and research. It offers an inter-disciplinary platform covering all four areas of practice, policy, education, research, and their nexus. The publications in this series are related to critical areas of sustainability, urban studies, planning, and urban geography. This book series aims to put together cutting-edge research findings linked to the overarching field of urban sustainability. The scope and nature of the topic are broad and interdisciplinary and bring together various associated disciplines from sustainable development, environmental sciences, urbanism, etc. With many advanced research findings in the field, there is a need to put together various discussions and contributions on specific sustainability fields, covering a good range of topics on sustainable development, sustainable urbanism, and urban sustainability. Despite the broad range of issues, we note the importance of practical and policyoriented directions, extending the literature and directions and pathways towards achieving urban sustainability. The series will appeal to urbanists, geographers, planners, engineers, architects, governmental authorities, policymakers, researchers of all levels, and to all of those interested in a wide-ranging overview of urban sustainability and its associated fields. The series includes monographs and edited volumes, covering a range of topics under the urban sustainability topic, which can also be used for teaching materials.

Tongyu Zhou · Yi Chen · Wu Deng · Ali Cheshmehzangi Editors

Smart Buildings and Technologies for Sustainable Cities in China

Editors Tongyu Zhou Department of Architecture and Built Environment University of Nottingham Ningbo China Ningbo, China Wu Deng Department of Architecture and Built Environment University of Nottingham Ningbo China Ningbo, China

Yi Chen College of Architecture and Urban Planning Tongji University Shanghai, China Ali Cheshmehzangi Qingdao City University Qingdao, China Hiroshima University Hiroshima, Japan

ISSN 2731-6483 ISSN 2731-6491 (electronic) Urban Sustainability ISBN 978-981-99-6390-4 ISBN 978-981-99-6391-1 (eBook) https://doi.org/10.1007/978-981-99-6391-1 This work was supported by ICT-Driven and Resilient Cities (IDRC) Project, The Ministry of Education, Culture, Sports, Science and Technology (MEXT), the Japan government. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore Paper in this product is recyclable.

Preface

As we stand on the precipice of a new era with climate change and environmental conservation, the collective challenge of achieving sustainable development has never been more imperative. This lies at the heart of this contributed volume, a journey that explores the intersection of advanced technology, urban development, and the vision of creating sustainable and smart cities in China. The rapid progress of urbanisation and the growing concern about climate change have put a spotlight on the role of sustainable practices in China, one of the world’s largest economies. The country is witnessing an ardent effort to balance its economic advancement with ecological preservation, aiming for peak carbon emission targets by 2030, while managing a fast urbanisation rate. The quest to meet these targets has led to the development of smart buildings, the cornerstone for building sustainable cities in China. These innovative structures prioritise energy efficiency, performance monitoring, and user-centric services. Beyond the buildings themselves, this evolution is significantly influencing energy performance, sustainability, and digital transformation at varying city levels. This contributed volume provides an exploration of the sophisticated technologies involved in smart buildings and their invaluable contribution to the creation of sustainable cities in China. It uncovers the roles of advanced digital technologies, such as the Internet of Things (IoT), Artificial Intelligence (AI), and big data, in monitoring and managing building performance, achieving energy efficiency, and enhancing sustainability. We journey through detailed discussions and case studies on a range of subjects. The content is thoughtfully arranged into three parts, each focussed on a unique facet of sustainability in the context of buildings and cities. The chapters that follow offer a rich tapestry of knowledge, expertly woven together by the brilliant minds of leading researchers and professionals. As we delve into topics like digital twins, data anonymisation, smart urban heat adaptation systems, and data-driven facility management, we do not just explore the technical aspects of smart buildings. Instead, we also seek to understand their role in creating a more sustainable, efficient, and inclusive urban environment.

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This contributed volume is more than just a collection of research and insights. It is an invitation to embark on a journey of exploration and discovery. A journey that will take us deep into the heart of smart buildings and sustainable cities, helping us understand not just what they are, but what they can be. As we traverse through these pages, we hope to inspire our readers—researchers, technology enthusiasts, professionals in the field, policymakers, students studying in a related, and all those vested in the future of sustainable urbanisation. We hope this book serves as an invaluable resource, a catalyst for change and a guide to navigating the uncharted terrains of a sustainable urban future. This journey is more than an academic exploration; it is a testament to the role that each one of us can play in shaping the future of our cities and our planet. We are all architects of our future, and as we embark on this journey, we hope that this book provides the insights, knowledge, and inspiration needed to create a sustainable and resilient future. This work represents the collective endeavour of many dedicated individuals whose invaluable contributions have made it a reality. We are immensely grateful to everyone involved. We extend our profound gratitude to all the contributors whose invaluable insights and dedication have made this book possible. Their passion for sustainable urbanisation and smart technology has illuminated the pages of this book, and their tireless work is greatly appreciated. We want to express our heartfelt thanks to our colleagues from the Centre for English Language Education at the University of Nottingham Ningbo China—Dr. Shanru Yang, Dr. Peter Sturman, and Dr. Luke Errington. Their meticulous proofreading has significantly improved the clarity and coherence of the book. Our appreciation also extends to Zhen Liang, an exceptional student from the Architectural Environment Engineering course. Her assistance with the book’s layout truly enhanced the visual presentation of this work. Last but not least, we thank the publishing team for their patience and continuous support. We hope you find this book as enlightening to read as it was for us to edit. May it guide you in your journey towards creating a more sustainable future. Ningbo, China Shanghai, China Ningbo, China Qingdao, China

Tongyu Zhou Yi Chen Wu Deng Ali Cheshmehzangi

Contents

Insights About Smart Buildings and Sustainable Cities: An Editorial to the Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tongyu Zhou, Yi Chen, Wu Deng, and Ali Cheshmehzangi

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Exploring the Smart and Sustainable Nexus in Buildings and Cities China’s Role in Combating Global Climate Change: Pathways to Reducing Carbon Emissions in Buildings . . . . . . . . . . . . . . . . . . . . . . . . . . Youwei Wang, Tongyu Zhou, and Ruiming Zhang

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Exploring the Emergence of Digital Twins in the Construction Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jingwen Sun, Tongyu Zhou, Thushini Mendis, and Isaac Lun

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Data Anonymization and Open Sharing Are Key to a Sustainable Built Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fazel Khayatian

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Modeling of Building System Operational Faults for Improved Energy Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rongpeng Zhang, Yu Yang, and Chengkai Lin

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Innovative Technologies for Enhancing Sustainability in Buildings and Cities Urban Heat Adaptation and a Smart Decision Support Framework . . . . Bao-Jie He, Ke Xiong, and Xin Dong

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Smart Heating in Centralized Urban Heating Systems . . . . . . . . . . . . . . . . Xiaojie Lin, Jiaying Chen, Wei Huang, and Wei Zhong

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The Application of n-D BIM in Chinese Construction Projects . . . . . . . . . Baotian Chang, Byung Gyoo Kang, and Nan Zhang

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Contents

A Review of the Shading Adjustment Occupant Behavior Model and Evaluation Method in Office Buildings . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Gaoxiang Chen, Jun Lu, Maycon Sedraz, and Zhiang Zhang Case Studies in Sustainability Real-Time Chiller Optimization in an Industrial Plant with Data-Driven Load Forecast Approach: A Case Study . . . . . . . . . . . . . 131 Siliang Lu Sustainability: Design Strategies and Applications in a Shanghai Commercial Complex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 Junqiang Wang, Yuran Kong, and Yang Jiao An Integrated and Intelligent Information Model-Based Smart University Campus and Its Digitalization Process . . . . . . . . . . . . . . . . . . . . . 155 Georgios Kapogiannis, Nan Lu, Cesar Augusto, Thapa Sudhir, Ravil Misalimov, Novianti, and Tianlun Yang

Editors and Contributors

About the Editors Tongyu Zhou is an Assistant Professor at the Department of Architecture and Built Environment at the University of Nottingham Ningbo China. He is a member of the China Green Building Council and ASHRAE, and holds WELL AP credential. He has a Ph.D. in Sustainable Energy and Building Technologies from the University of Nottingham. With a multidisciplinary educational background and industrial experience in computer science, HVAC systems, and intelligent buildings, his research interests primarily focus on low-carbon buildings, occupant behaviours and thermal comfort in built environments, human-building interaction, and building simulation. Yi Chen is a Full Professor at the College of Architecture and Urban Planning (CAUP) of Tongji University, and is a 1st Class Registered Architect of P.R. China. Prof. Chen is a member of the Academic Committee of CAUP, Director of Chinese side of the double master degree programme between Pavia University and Tongji University; Director of Sino-UK Institute of Sustainability at Tongji University; and a member of Environmental Art Design Committee of China Artists Association. Wu Deng is an Associate Professor at the Department of Architecture and Built Environment at the University of Nottingham Ningbo China (UNNC). He has Ph.D. in sustainable built environment from the University of New South Wales (UNSW), Australia. Prior to joining UNNC, he worked as an Urban Design Studio Tutor and Sessional Lecturer at UNSW, and then a Lead Consultant/Technical Manager in the Eco-city Research Center, Siemens Corporate Technology. His research interest lies primarily in three areas: (1) sustainable eco-city development; (2) building life cycle material, energy, and carbon modelling and analysis; and (3) building prototyping and modelling. Ali Cheshmehzangi is the World’s top 2% field leader, recognised by Stanford University. He has recently taken a senior leadership and management role at Qingdao

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City University (QCU), where he is a Professor in Urban Planning, Director of the Center for Innovation in Teaching, Learning, and Research, and Advisor to the school’s international communications. Over 11 years at his previous institute, he was a Full Professor in Architecture and Urban Design, Head of the Department of Architecture and Built Environment, Founding Director of the Urban Innovation Lab, Director of Center for Sustainable Energy Technologies, and Interim Director of Digital Design Lab. He was Visiting Professor and now Research Associate of the Network for Education and Research on Peace and Sustainability (NERPS) at Hiroshima University, Japan. He is globally known for his research on ‘urban sustainability’. So far, he has published over 300 journal papers, articles, conference papers, book chapters, and reports. To date, he has 17 other published books.

Contributors Cesar Augusto University of Nottingham Ningbo China, Ningbo, China Baotian Chang Beijing University of Technology, Beijing, China Gaoxiang Chen Department of Architecture and Built Environment, University of Nottingham Ningbo China, Ningbo, China Jiaying Chen College of Energy Engineering, Zhejiang University, Hangzhou, China Yi Chen College of Architecture and Urban Planning, Tongji University, Shanghai, China Ali Cheshmehzangi Qingdao City University, Qingdao, China; Hiroshima University, Hiroshima, Japan Wu Deng Department of Architecture and Built Environment, University of Nottingham Ningbo China, Ningbo, China Xin Dong Centre for Climate-Resilient and Low-Carbon Cities, School of Architecture and Urban Planning, Chongqing University, Chongqing, China Bao-Jie He Centre for Climate-Resilient and Low-Carbon Cities, School of Architecture and Urban Planning, Chongqing University, Chongqing, China; Network for Education and Research On Peace and Sustainability (NERPS), Hiroshima University, Hiroshima, Japan; Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education, Chongqing University, Chongqing, China; State Key Laboratory of Subtropical Building Science, South China University of Technology, Guangzhou, China Wei Huang College of Energy Engineering, Zhejiang University, Hangzhou, China Yang Jiao TIANHUA Architecture Planning & Engineering Ltd., Shanghai, China

Editors and Contributors

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Byung Gyoo Kang University of Nottingham Ningbo China, Ningbo, China Georgios Kapogiannis School of Business and Leadership, Oryx Universal College in partnership with Liverpool John Moores University, Doha, Qatar Fazel Khayatian Urban Energy Systems Laboratory, Empa-Swiss Federal Laboratories for Materials Science and Technology, Dübendorf, Switzerland Yuran Kong TIANHUA Architecture Planning & Engineering Ltd., Shanghai, China Chengkai Lin School of Architecture and Planning, Hunan University, Changsha, China Xiaojie Lin College of Energy Engineering, Zhejiang University, Hangzhou, China Jun Lu Department of Architecture and Built Environment, University of Nottingham Ningbo China, Ningbo, China Nan Lu University of Nottingham Ningbo China, Ningbo, China Siliang Lu Bosch Center for Artificial Intelligence, Shanghai, China Isaac Lun Department of Architecture and Built Environment, University of Nottingham Ningbo China, Ningbo, China Thushini Mendis Department of Architecture and Built Environment, University of Nottingham Ningbo China, Ningbo, China Ravil Misalimov University of Nottingham Ningbo China, Ningbo, China Novianti University of Nottingham Ningbo China, Ningbo, China Maycon Sedraz Department of Architecture and Built Environment, University of Nottingham Ningbo China, Ningbo, China Thapa Sudhir University of Nottingham Ningbo China, Ningbo, China Jingwen Sun Department of Architecture and Built Environment, University of Nottingham Ningbo China, Ningbo, China Junqiang Wang TIANHUA Architecture Planning & Engineering Ltd., Shanghai, China Youwei Wang China Green Building Council, Beijing, China Ke Xiong Centre for Climate-Resilient and Low-Carbon Cities, School of Architecture and Urban Planning, Chongqing University, Chongqing, China Tianlun Yang NingboTech University, Ningbo, China Yu Yang School of Architecture and Planning, Hunan University, Changsha, China Nan Zhang Beijing University of Technology, Beijing, China

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Rongpeng Zhang School of Architecture and Planning, Hunan University, Changsha, China Ruiming Zhang Department of Architecture and Built Environment, University of Nottingham Ningbo China, Ningbo, China Zhiang Zhang Department of Architecture and Built Environment, University of Nottingham Ningbo China, Ningbo, China Wei Zhong College of Energy Engineering, Zhejiang University, Hangzhou, China Tongyu Zhou Department of Architecture and Built Environment, University of Nottingham Ningbo China, Ningbo, China

List of Figures

Exploring the Emergence of Digital Twins in the Construction Industry Fig. 1

Concept map of digital twin [10] . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Data Anonymization and Open Sharing Are Key to a Sustainable Built Environment Fig. 1

Approaches to preserving privacy in data . . . . . . . . . . . . . . . . . . . . . .

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Modeling of Building System Operational Faults for Improved Energy Efficiency Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6

Potential HVAC operational faults of a VAV system in a central plant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Impact of economizer outdoor air sensor offset on building cooling energy consumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Impact of the integrated thermostat/humidistat offset faults on the building cooling and heating energy consumption . . . . . . . . . Impact of the integrated thermostat/humidistat offset faults on the building’s cooling and heating energy consumption . . . . . . . . Impact of coil fouling on indoor thermal comfort . . . . . . . . . . . . . . . Impact of an air filter fouling on fan energy consumption . . . . . . . .

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Urban Heat Adaptation and a Smart Decision Support Framework Fig. 1 Fig. 2 Fig. 3

Actions towards heat resilient cities and societies . . . . . . . . . . . . . . . Heat safety guidelines for low-risk acclimatized individuals in different climate zones based on ACSM guidelines [12] . . . . . . . Associations of heat stresses with physiological equivalent temperature (PET) and Universal Thermal Climate Index (UTCI) [3, 22, 23] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Fig. 4 Fig. 5 Fig. 6 Fig. 7 Fig. 8

List of Figures

A framework of the smart decision support tool for urban heat adaptation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Environmental, social, and economic data and the associated sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . An ideal neighbourhood with spatially heterogeneous morphological characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Simulated outdoor thermal comfort (PET) at 14:00 (top) and 12:00 (down) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The priorities of the paths according to the maximum outdoor thermal comfort level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Smart Heating in Centralized Urban Heating Systems Fig. 1 Fig. 2 Fig. 3 Fig. 4

Illustration of UCHS in northern China [12] . . . . . . . . . . . . . . . . . . . A framework of CPS-Based UCHS control platform used in heating system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Role of CPS-based platform in UCHS . . . . . . . . . . . . . . . . . . . . . . . . An integrated energy system case in an industrial park and its CPS-based operation platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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The Application of n-D BIM in Chinese Construction Projects Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5

Distribution of respondents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Distribution of respondents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The difference analysis of professions . . . . . . . . . . . . . . . . . . . . . . . . The multiple response analysis of driving and hindering factors . . . Chi-square analysis of hindering factors . . . . . . . . . . . . . . . . . . . . . . .

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A Review of the Shading Adjustment Occupant Behavior Model and Evaluation Method in Office Buildings Fig. 1

The methods of occupant behavior model . . . . . . . . . . . . . . . . . . . . .

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Real-Time Chiller Optimization in an Industrial Plant with Data-Driven Load Forecast Approach: A Case Study Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7 Fig. 8 Fig. 9

Plant overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Basic diagram of the HVAC system in the plant . . . . . . . . . . . . . . . . Pipeline of cooling load forecast model . . . . . . . . . . . . . . . . . . . . . . . Real-time control diagram with cooling load forecast model . . . . . . COP benchmark between 2020 and 2021 . . . . . . . . . . . . . . . . . . . . . . Annual COP benchmark between 2020 and 2021 . . . . . . . . . . . . . . . Energy benchmark in January between baseline and proposed solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Performances of cooling load forecast model . . . . . . . . . . . . . . . . . . Hybrid modelling design pattern for the solution . . . . . . . . . . . . . . .

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List of Figures

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Sustainability: Design Strategies and Applications in a Shanghai Commercial Complex Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5

Project design concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Project rendering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Major sponge infrastructures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Carbon emission calculation of life cycle (tCO2 ) . . . . . . . . . . . . . . . . Natural ventilation simulation of the hotel . . . . . . . . . . . . . . . . . . . . .

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An Integrated and Intelligent Information Model-Based Smart University Campus and Its Digitalization Process Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7 Fig. 8 Fig. 9

Understand the demands and requirements . . . . . . . . . . . . . . . . . . . . Developing process of the smart campus . . . . . . . . . . . . . . . . . . . . . . The digitalization process of the smart campus . . . . . . . . . . . . . . . . . QGIS process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Recap and Google earth process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . DTM outline process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Infrastructure model process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Coordinated model in Autodesk Revit . . . . . . . . . . . . . . . . . . . . . . . . InfraWorks process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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List of Tables

Exploring the Emergence of Digital Twins in the Construction Industry Table 1

Keywords related to the application of digital twins in the construction industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Smart Heating in Centralized Urban Heating Systems Table 1 Table 2

Comparison between CPS-based smart control and conventional control in UCHS . . . . . . . . . . . . . . . . . . . . . . . . . . Case industrial park integrated energy system key parameters . . . .

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The Application of n-D BIM in Chinese Construction Projects Table 1 Table 2 Table 3 Table 4 Table 5 Table 6

Basic information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Distribution of working and applying BIM years . . . . . . . . . . . . . . Descriptive statistics of current situation . . . . . . . . . . . . . . . . . . . . . Driving factors and hindering factors of n-D BIM . . . . . . . . . . . . . SWOT of n-D BIM application based on literature review . . . . . . . SWOT of n-D BIM application based on literature review and survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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A Review of the Shading Adjustment Occupant Behavior Model and Evaluation Method in Office Buildings Table 1

Characteristics and limitations of the occupant behavior models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Real-Time Chiller Optimization in an Industrial Plant with Data-Driven Load Forecast Approach: A Case Study Table 1

The features extracted from the database . . . . . . . . . . . . . . . . . . . . .

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List of Tables

Sustainability: Design Strategies and Applications in a Shanghai Commercial Complex Table 1 Table 2

Project water consumption of fixtures and fittings . . . . . . . . . . . . . Newly added window of the theater . . . . . . . . . . . . . . . . . . . . . . . . .

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Insights About Smart Buildings and Sustainable Cities: An Editorial to the Book Tongyu Zhou, Yi Chen, Wu Deng, and Ali Cheshmehzangi

In the face of rapid urbanisation and increasing carbon emissions, China stands at a critical juncture in its quest for sustainable growth. As one of the world’s largest economies, the country is grappling with the intricate task of balancing economic development with ecological preservation. This undertaking is increasingly pressing as the country aims to reach peak carbon emission targets by 2030, while simultaneously dealing with an expected urbanisation rate of 70% and a city population of approximately 1.1 billion. This scenario presents an array of challenges, chief among them being the need to confront rising building service demands, the pursuit of improved comfort lifestyles, energy supplies, and the consequent carbon emission. One avenue through which China addresses these challenges is the development of smart buildings. Recognised as pivotal components for sustainable and smart cities, these buildings prioritise energy efficiency, building performance monitoring and management, and user-centric services. In this book, we explore the multifaceted nature of smart buildings and their contribution to sustainable cities in China. We delve into the role of various stakeholders, including researchers, architects, building service engineers, urban planners, policymakers, markets, and society at large, in fostering this development. Furthermore, we examine the impact of smart buildings on energy performance, sustainability, and digital transformation at different city levels. T. Zhou (B) · W. Deng Department of Architecture and Built Environment, University of Nottingham Ningbo China, Ningbo, China e-mail: [email protected] Y. Chen College of Architecture and Urban Planning, Tongji University, Shanghai, China A. Cheshmehzangi Qingdao City University, Qingdao, China Hiroshima University, Hiroshima, Japan © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 T. Zhou et al. (eds.), Smart Buildings and Technologies for Sustainable Cities in China, Urban Sustainability, https://doi.org/10.1007/978-981-99-6391-1_1

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The following chapters will continue to delve deeper into these subjects, comprehensively examining the challenges, opportunities, and innovations that define the journey toward smart buildings and sustainable cities in China. As we navigate through this exploration, we hope that this book will serve as a valuable resource and guide for policymakers, researchers, urban planners, and stakeholders invested in the future of sustainable urbanisation.

1 A General Overview As we stand at the precipice of a new era in human history, where the issues of sustainability, energy efficiency, and climate change are not just pressing concerns but existential threats, the importance of adopting a more sustainable approach in our built environment cannot be overstated. The buildings we inhabit are not just static entities but dynamic living systems that interact with their environment, inhabitants, and each other. Therefore, creating a sustainable built environment is not a singular act but a continuous process. As China continues to urbanise and strive for peak carbon emissions by 2030, developing smart buildings and sustainable cities will play a crucial role. These buildings and cities will help China achieve its environmental goals and improve its citizens’ quality of life. The development of smart buildings and sustainable cities is about more than just technology. It is also about policy, economics, and society. It requires the involvement of various stakeholders, including the government, markets, and society. The government plays a crucial role in setting the policy framework and providing the necessary incentives. The markets, including the real estate and construction industries, implement these policies and bring smart buildings to life. Society, including the residents of these buildings and cities, are the ultimate beneficiaries. Their acceptance and adoption of smart buildings and sustainable cities are critical to their success.

2 The Role of Smart Buildings Smart buildings are at the heart of sustainable cities. They are constructed to be comfortable, healthy, and productive environments for occupants while minimising their environmental impact. The role of smart buildings in sustainable cities can be seen in three main areas: energy performance, sustainability, and digital transformation. Smart buildings are designed to be highly energy efficient. They utilise advanced technologies, such as energy-efficient appliances and systems, renewable energy sources, and energy management systems, to minimise energy consumption. This not only reduces carbon emissions but also lowers their operating costs, making them more economically viable.

Insights About Smart Buildings and Sustainable Cities: An Editorial …

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In terms of sustainability, smart buildings are designed to be environmentally friendly. They utilise sustainable materials in their construction, incorporate green spaces, and minimise waste and pollution. They also consider the health and wellbeing of their occupants, providing a comfortable and healthy indoor environment. In terms of digital transformation, smart buildings utilise advanced digital technologies, such as the Internet of Things (IoT), artificial intelligence (AI), and big data, to monitor and manage their performance. These technologies enable the buildings to adapt to changing conditions and needs, improving their efficiency and effectiveness.

3 The Role of Sustainable Cities Cities have become the focal point of the global sustainability challenge, accounting for a significant share of the world’s energy consumption and greenhouse gas emissions. Yet, paradoxically, they also represent the most promising avenues for addressing these concerns, mainly through the development of sustainable buildings. The concept of sustainable cities encompasses much more than simply reducing carbon footprints. It entails a vision of urban life that combines the efficient use of resources, protection of the environment, and the creation of a high quality of life for all residents. In this context, the building sector plays a fundamental role. Sustainable cities are largely defined by their sustainable buildings, designed to minimise environmental impact, maximise resource efficiency, and improve the quality of life for occupants. By reducing cities’ overall energy consumption, water consumption, and emissions, these buildings contribute to the sustainability of cities. Equipped with advanced technologies and green designs, smart buildings are one of the key components of a sustainable city. They use energy-efficient appliances, incorporate renewable energy sources, such as solar panels, and use technologies that regulate energy usage based on occupancy. They also implement systems for efficient water use, such as rainwater harvesting and gray water recycling, contributing to significant water savings. All these elements work together to create buildings that not only have a lower environmental impact but also provide a healthier and more productive environment for occupants. Beyond individual buildings, sustainable urban planning in a city also considers the city’s overall architecture. It involves designing building clusters, neighbourhoods, and districts to increase energy efficiency at a larger scale. This could involve, for instance, the strategic placement of buildings to maximise natural light and reduce heating needs or the use of shared green spaces that provide natural cooling and improve air quality. The role of sustainable cities includes an important social dimension. Sustainable buildings within these cities are often designed with inclusivity and accessibility in mind. This can involve the provision of affordable housing, access to green spaces, and integrating facilities such as schools and hospitals within residential areas. All these aspects contribute to a more liveable and equitable city.

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Sustainable cities, particularly through the development of buildings, play an integral role in addressing the global sustainability challenge. Each building, designed and built with sustainability in mind, acts as a brick in the enormous edifice of a sustainable city, contributing to an environmentally friendly, socially inclusive, and economically viable urban future.

4 A Look Inside This Book This book is an interdisciplinary exploration of the latest advancements and challenges in creating a sustainable built environment in China. It draws upon the expertise of leading researchers and professionals in the field, encompassing a wide range of topics, including digital twins, data anonymisation, operational fault modelling, smart urban heat adaptation systems, energy-efficient heating in urban systems, and the application of data-driven technologies in facility management. This book is intended as a comprehensive guide to the latest theories, technologies, and practices in sustainable building and city designs. The chapters are divided into three parts, each focusing on a unique aspect of sustainability in the context of buildings and cities. Part I–Exploring the Smart and Sustainable Nexus in Buildings and Cities provides the foundational knowledge to understanding the intersection of smart and sustainable approaches in the context of built environments. This part consists of four chapters. Chapter 2 delves into China’s role in combating global climate change, focusing on the pathways to reducing carbon emissions in buildings. It draws on the experience of the development of green low-carbon buildings in the past decade and discusses crucial aspects of China’s building carbon emission reduction initiatives. Chapter 3 focuses on the emerging role of digital twins in the construction industry. It compares the unique and shared features of Building Information Modelling (BIM) and digital twins, and reviews their influence across the construction process stages, aiming to propel industry development and promote sustainable practices. Chapter 4 emphasises the importance of data anonymisation and open sharing in achieving a sustainable built environment. It reviews the latest advancements in data anonymisation and contrasts their benefits and shortcomings against the existing methods. Chapter 5 focuses on the role of modelling and simulation technology in detecting and diagnosing building system operational faults. It discusses the reasons and risks of operational faults and various fault detection and diagnostic methods. Part II–Innovative Technologies for Enhancing Sustainability in Buildings and Cities takes a deep dive into the advanced technologies being used to drive sustainable buildings and cities. It includes four chapters. Chapter 6 presents a framework for the development of a smart urban heat adaptation decision-making system for sustainable and resilient communities. The

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chapter reviews existing decision-support tools related to urban heat mitigation and adaptation and outlines challenges and recommendations for future research. Chapter 7 investigates smart heating within centralised urban heating systems, focusing on the context of China’s carbon neutrality roadmap. It introduces the latest work on clean heating technology review, data-driven modelling, flexibility analysis, operation decoupling optimisation, and the transition from centralised heating systems to integrated energy systems. Chapter 8 explores the application of n-D Building Information Modeling (BIM) in Chinese construction projects, presenting a comprehensive review of its latest advancements and applications. Chapter 9 concentrates on the role of human behaviour in energy consumption in office buildings, specifically the use of movable shading devices. It highlights the necessity for more accurate estimations of energy consumption considering the stochastic nature of human behaviour. Part III–Case Studies in Sustainability comprises of case studies that illuminate the practical applications of theories and technologies. It includes three chapters. Chapter 10 presents a case study of real-time chiller optimisation in the manufacturing industry using data-driven load forecasting. It provides insights into how digital technologies and data-driven algorithms can facilitate energy conservation and efficiency in industrial operations. Chapter 11 showcases sustainable design strategies and their application in a commercial complex in Shanghai China. The chapter presents advanced design concepts and sustainability strategies that helped the project reach high levels in the Chinese Green Building assessment system. Chapter 12 discusses the integrated and intelligent information model-based smart-university campus and its digitalisation process. It explains how the implementation of technologies such as the IoT, common data environment, cloud computing, and data mining can contribute to the development of smart campuses.

5 Beyond the Book: Future Directions The advancements in smart buildings and sustainable cities presented in this book demonstrate the significant strides made in recent years. As we look to the future, this field will continue to evolve, presenting numerous opportunities for further exploration and innovation. Emerging technologies such as AI and Machine Learning (ML) are set to play an increasingly important role in the sustainable built environment. These technologies can potentially revolutionise how we design, construct, and manage buildings. For instance, AI can optimise energy usage in real-time, predicting and adjusting to changes in weather, occupancy, and equipment performance. In addition, ML can be used to analyse large data sets, identify patterns, and predict future energy usage. Another area of interest is the role of the IoT in sustainable building management. The IoT enables devices to connect, communicate, and cooperate, creating a network

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of devices that can monitor and control various aspects of a building’s operation. This leads to more efficient use of resources and improved comfort and productivity for building occupants. Looking ahead, the challenge is to continue pushing the boundaries of what is possible in the design, construction, and operation of smart buildings and sustainable cities. It will require a shift in mindset from viewing buildings as static entities to seeing them as dynamic, interactive systems that are part of a larger ecosystem. Much work is needed, but the potential rewards are immense. By creating a more sustainable built and city, we can mitigate climate change, improve health and wellbeing, create more equitable societies, and foster economic growth and resilience.

6 Final Thoughts As we embark on this journey through the development of smart buildings and sustainable cities in China, we are reminded of the importance of this work. The challenges we face are significant, but so too are the opportunities. By harnessing the power of technology, policy, and society, we can create buildings and cities that are not only sustainable but also vibrant, inclusive, and prosperous. The information compiled in this book is intended to significantly impact how professionals in the field approach smart buildings and sustainable cities. By presenting a diverse range of topics, we aim to comprehensively understand the challenges, opportunities, and emerging trends in this field. We hope readers will find this book a valuable resource, providing insights and guidance that can be directly applied in their work. Whether you are an architect, an engineer, a facilities manager, or a student studying in a related field, we believe the content presented in this book will deepen your understanding of sustainable building and city practices and inspire innovative solutions for the challenges of tomorrow. We also hope that it will spark further research and action, contributing to the ongoing effort to create a more sustainable and resilient future. In the end, the development of smart buildings and sustainable cities is not just about buildings and cities. It is about people-those who live and work in these buildings and cities, those who design and build them, and those who make the decisions that shape their future. By focusing on people, we can ensure that our buildings and cities are not only smart and sustainable but also places where people can thrive.

Exploring the Smart and Sustainable Nexus in Buildings and Cities

China’s Role in Combating Global Climate Change: Pathways to Reducing Carbon Emissions in Buildings Youwei Wang, Tongyu Zhou, and Ruiming Zhang

Abstract Climate change is primarily caused by the accumulation of carbon emissions over the 200-year period since the advent of industrial civilization. To address climate change, nations must cooperate in achieving low-carbon development. As a significant player in global carbon reduction efforts, China pledges to reach peak carbon emissions by 2030, achieve carbon neutrality by 2060, and has proposed detailed implementation strategies. Over the past decade, China has built a tremendous number of green buildings, reducing energy consumption and carbon emissions in the construction sector. In the next ten years, China’s urbanisation rate will continue to rise, and the urban population will further increase. Therefore, further reducing building carbon emissions is one of the crucial pathways to achieving carbon peaking and carbon neutrality in China. Drawing on the development experience of green lowcarbon buildings in the past ten years, this work discusses several important aspects in China’s implementation of building carbon emission reduction from the following perspectives: focusing on the key stage in the whole life cycle of buildings, the significance of actual measured energy consumption in buildings, renewable energy as an essential approach to achieving building carbon reduction, carbon emission factors, carbon emission characterisation, and carbon trading and carbon quotas. Keyword Climate change · Carbon emissions · Carbon peaking and carbon neutrality · China · Building

Y. Wang China Green Building Council, Beijing, China T. Zhou (B) · R. Zhang Department of Architecture and Built Environment, University of Nottingham Ningbo China, Ningbo, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 T. Zhou et al. (eds.), Smart Buildings and Technologies for Sustainable Cities in China, Urban Sustainability, https://doi.org/10.1007/978-981-99-6391-1_2

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1 Background Since the advent of industrial civilization, the environmental impact of fossil fuel usage, particularly coal and oil, has become increasingly prominent due to the rapid growth in global demand. Entering the twenty-first century, it has brought the issue of climate change to the forefront of global discourse, transforming it into one of the most noticeable concerns in the world. Although climate change is an inherent aspect of Earth’s natural evolution, driven by natural forces over the long term with an average cycle of approximately a few centuries [1], human activities (especially the use of fossil fuels) since the Industrial Revolution have significantly increased greenhouse gas emissions, intensifying the greenhouse effect and accelerating climate change. Thus, it is essential to consider anthropogenic factors when examining climate change. As defined by the United Nations Framework Convention on Climate Change (UNFCCC), climate change refers to alterations in the global atmospheric composition caused directly or indirectly by human activities, resulting in “a change of climate which is attributed directly or indirectly to human activity that alters the composition of the global atmosphere and which is in addition to natural climate variability observed over comparable time periods” [13]. According to the Integrated Carbon Observation System, since 1750, approximately 1,700 gigatons of carbon have been emitted globally, with 531 gigatons from Europe, 416 gigatons from the United States, and 235 gigatons from China [7]. Developed countries have released substantial amounts of CO2 since the Industrial Revolution, making them the primary contributors to the current climate change. Climate change can lead to multiple effects on ecosystems and the natural environment by altering climate elements such as light, heat, moisture, and wind speed, as well as their spatiotemporal distributions. A report by the United Nations Intergovernmental Panel on Climate Change indicates that if greenhouse gas emissions are not significantly reduced by 2030, global warming will cause the average temperature to rise 1.5 °C above pre-industrial levels in the following decades, resulting in irreversible ecological damage and triggering crises for the whole humanity [11]. If the global average temperature rises by 1.5 °C, the Arctic may experience ice-free summers this century, and 70–80% of coral reefs could vanish. With the temperature increase of 1.5 °C, the global sea level at the end of this century will rise by 0.26–0.77 m compared with the average level at the beginning of this century. Furthermore, global warming does not equate to uniform warming worldwide. In general, the temperature growth range of land is larger than that of the ocean, and in the mid-to-high latitude land regions it is larger than in low-latitude areas, which means that extreme weather will occur more frequently. For example, if the global average temperature rises by 1.5 °C, minimum temperatures in high latitudes will rise by 4.5 °C, and maximum temperatures in the mid-latitudes of the world will increase by 3 °C [8]. Climate change has been given high priority by the majority of countries around the world, with 193 countries having signed the Paris Agreement. However, there is

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still a long way to go to reduce carbon emissions. According to the updated Nationally Determined Contributions (NDCs) by 151 parties, based on the data available at the end of 2021, global carbon dioxide emissions are still projected to be 13.7% higher in 2030 than in 2010, making the goal of reducing emissions by 45% to limit global warming to 1.5 °C a challenging task [14]. Addressing climate change necessitates collaboration among all nations, organizations, and individuals, as no single entity can tackle this global threat alone or remain unaffected. The transition to a low-carbon economy in response to long-term emission mitigation goals should not be viewed as a constraint on economic and social development. Instead, it should be seen as an opportunity and a fundamental path to sustainable development for all nations.

2 Low Carbon Development in China Low-carbon development is not only a global concern but also a pressing issue for China, as its essential interests are closely aligned with global trends. This alignment fundamentally determines that China should play a significant role in low-carbon development, as it could otherwise lose a valuable historical opportunity to enhance its competitiveness on a global scale. Consequently, the Chinese government has adopted a proactive, scientific, and pragmatic approach to addressing the issue of climate change. China’s role in global low-carbon development is decided by its size, endowment, capabilities, comparative advantages, and willingness to adapt. However, the ultimate outcome depends on the effectiveness of governance and the wisdom and efforts of its people as a whole. China must continuously learn from exemplary countries and contribute to a sustainable global civilization. It is necessary for China to emphasise strategic thinking, and adhere to reform and the opening-up policy. Recently, the Chinese government has set clear objectives for carbon peaking and carbon neutrality for the next decade [3]: ● By 2025, energy consumption per unit of GDP in China will decrease by 13.5% compared to 2020, CO2 emissions per unit of GDP will decline by 18%, the proportion of non-fossil energy consumption will reach approximately 20%, forest coverage will reach 24.1%, and forest stock will amount to 18 billion m3 . ● By 2030, CO2 emissions per unit of GDP will decrease by more than 65% compared to 2005, the proportion of non-fossil energy consumption will reach approximately 25%, the total installed capacity of wind and solar power will exceed 1,200 GW, forest coverage will reach about 25%, forest stock will reach 19 billion m3 , and carbon dioxide emissions will peak and gradually decline thereafter. According to International Monetary Fund (IMF) estimates [9], China’s GDP growth rate is projected to remain at a relatively high level of around 5% until at least 2030, significantly higher than the less than 3% growth rate of developed countries

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at their peak. Until that time, the annual decline rate of CO2 intensity per unit of GDP must correspondingly be maintained at a relatively high level. In 2020, China’s CO2 intensity per unit of GDP decreased by 18.8% compared to 2015, with an average annual decline rate of nearly 4%, exceeding China’s commitment to the international community [4]. Based on a relatively optimistic estimate, as the industrial structure adjusts, the annual decrease in CO2 intensity per unit of GDP is expected to increase, potentially reaching a level of 4.5 to 5.5% by around 2030 [5]. This may lead to CO2 emissions peaking before 2030. The authors believe that China’s commitment to the international community about “carbon peaking and carbon neutrality” holds several significant implications for the country: ● It demonstrates that China is trustworthy with regard to its environmental responsibilities. It is working to build a community with a shared future for mankind. ● Energy security is vitally important to China’s national security. Achieving carbon peaking and carbon neutrality is an imperative pathway for China’s energy transition and energy independence. ● Achieving carbon peaking and carbon neutrality is an important part in adjusting energy and industrial structures. Over the past forty years, China’s development has largely relied on high-energy-consuming, high-emission, and highly polluting industries such as real estate, steel, cement, and heavy chemical industries. In the future, the optimisation and adjustment of the industrial structure will be of utmost importance. China needs to transition away from its reliance on traditional industries, emphasizing instead high-tech and emerging industries and prioritising sustainable energy and environmental protection. ● Achieving carbon peaking and carbon neutrality can significantly improve the environment. Over the past few years, we can see that significant progress has been made in China’s ecological environment. Taking air quality as an example, the national average PM2.5 concentration is now around 30 µg/m3 , a reduction of around 35% compared to 2015 [5]. Experts predict that after achieving carbon neutrality, China’s PM2.5 will be well below 10 µg/m3 , reaching the same level as developed countries [2]. Consequently, the incidence of diseases caused by air quality, such as upper respiratory infections and bronchitis, will be substantially reduced.

3 Important Concepts of Building Carbon Emission Carbon emissions arise from three main sectors: industry, construction, and transportation. The scale of these sectors varies among countries, resulting in different levels of carbon emissions. In China, these three sectors account for approximately one-third of emissions each. In recent years, the Chinese government has taken measures to adjust the country’s industrial structure. As for the construction industry, due to the process of urbanisation, it is expected that it will continue to develop at

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a rate of approximately 1 billion m2 per year for the next decade [10]. Therefore, achieving energy conservation and reducing carbon emissions in the construction sector is essential for China to reach peak carbon emissions and achieve the goal of carbon neutrality. In 2014, the nationally implemented Green Building Evaluation Standard was issued, signifying the official commencement of China’s efforts to reduce carbon emissions in the building sector. By the end of 2020, green buildings accounted for 77% of the annual newly built buildings in urban areas, and the cumulative green building area has exceeded 6.6 billion m2 . During the 13th Five-Year Plan period, the building energy-saving standards were further improved, completing energy-saving renovations for 0.5 billion m2 of existing residential buildings and 0.2 billion m2 of public buildings. The proportion of renewable energy use in residential buildings has reached 6% [15]. The authors believe that there is a need to reflect on the practical experiences of building sector carbon reduction over the past few years.

3.1 The Carbon Footprint of Buildings The whole life cycle of a building consists of seven phases: construction material production, material transportation, construction, operation, maintenance, demolition, and waste disposal, each of which consumes energy and produces carbon emissions. According to the literature, the operation phase plays a dominant role, accounting for 60–80% of the energy consumption and carbon emissions in the entire life cycle [6, 12]. The Green Building Committee of the Chinese Urban Science Association has conducted research on this subject. It collected and analysed data from over a dozen case studies in northern, central, and southern China, which have supported this conclusion. Hence, in the early stages of reducing building carbon emissions, it is not essential to excessively examine carbon emissions from the other phases, i.e. construction material production, material transportation, construction, maintenance, demolition, and waste disposal, as their share in the whole lifecycle is comparatively minor. The authors believe that concentrating on the operation phase is crucial for addressing the core issue.

3.2 Estimation of Energy Consumption in the Operation Phase The energy consumption during the operation phase is the fundamental data for calculations of building carbon emissions. However, in practice, the methodology for estimating the energy consumption of a building throughout its entire operation phase remains somewhat unclear. The energy consumption data must be submitted when applying for a green building design certification. However, as the building is

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not yet completed at the time, its energy consumption can only be a theoretical value based on the parameters provided by the designer. Theoretical calculations are usually made using domestic and international calculation programs. A typical calculation mainly relies on the following parameters: ● design data, such as orientation, shape factor, window-to-wall ratio, shading, heat transfer coefficient of walls/windows/roof, etc. ● local meteorological data, such as air temperature, humidity, wind speed, solar radiation, precipitation, etc. ● working conditions, such as indoor temperature, fresh air demand, internal gain, etc. However, the majority of energy consumption calculations are only for heating, cooling and lighting. Other forms of energy consumption, such as the use of domestic appliances, are basically not taken into account, not to mention changes of occupants, the variation of working hours, etc. Nowadays, there are many available building energy calculation programs, but due to different mathematical models and algorithms, the final results might vary greatly. The theoretical values of energy consumption have limitations and should only be of some reference value in the reviewing of green building design appraisals. To obtain a more credible picture of the energy consumption of a building, the actual measurements should be recorded for a whole year. The energy performance of a building varies from season to season, and usually, energy consumption refers to the amount of energy used per square metre per year. Therefore, actual energy consumption should be defined as the combined energy consumption (including electricity, gas, oil, coal, hot water, etc.) measured under normal climatic conditions over a year, with relatively stable occupancy and working hours. Even so, because of the uncertainty of weather conditions, working conditions, building occupancies, the deterioration of the thermal insulation of construction materials, and especially the energy-related occupant behaviours (such as setting thermostats, opening windows, etc.), the measured values still vary. However, their accuracy tends to be much higher than the simulation calculation, and they have a greater reference value. Nonetheless, using actual measurements still requires further scientific consideration. For example, in the case of a five-star hotel, firstly the occupancy rate needs to be specified; Secondly, it is necessary to clarify the scope of the building area involved. If the underground garage is included, which usually uses much less energy than that of the main building, it will dilute the actual energy consumption per unit area of the hotel. Therefore, even measured energy consumption statistics should be used with care and rigour. In recent years, the Chinese government has attached importance to energy monitoring platforms, mainly for public buildings. This data has been very beneficial in promoting energy efficiency in buildings. More importantly, as more and more energy consumption data is collected, it forms a reliable database that can be used as a basis for carbon emission calculations. This is a great contribution towards carbon reduction for the whole world.

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3.3 Renewable Energy for Buildings Renewable energy is one of the main means of energy saving and carbon reduction, and solar energy is a key renewable energy source for buildings. There are two main factors involved in using solar energy in buildings. Firstly, the availability of solar energy depends on the intensity of solar radiation and the duration of sunshine in the area, and the local atmospheric conditions. Secondly, the use of solar energy depends on the height of a building, the type of roof, the quality of solar devices, etc. Despite some limitations in utilising solar energy in urban areas, eastern cities in China, such as Shanghai, have already established policies to promote the use of renewable energy, requiring the installation of solar photovoltaics (PV) on the roofs of newly constructed buildings. Practical experience shows that in China the overall energy-saving effect of solar PV in urban buildings is not satisfactory, mainly due to the low cost-performance ratio of solar PV and its low proportion in the total energy consumption of buildings. To address this issue, experts have proposed an alternative approach; establishing large solar energy farms in western China, where solar resources are abundant and land is cheap. By leveraging ultra-high-voltage transmission lines, the generated electricity can be conveyed over long distances from western to eastern China with minimal loss. This innovative strategy holds promise in transforming China’s energy landscape, mitigating dependency on fossil fuels and reducing carbon emissions. Furthermore, it enhances the diversification of energy sources, contributing to a more resilient and sustainable energy infrastructure.

3.4 Carbon Emission Factors The carbon emission factor, quantifying carbon emissions per unit of energy used, is a crucial parameter in the estimation of carbon emission. Owing to China’s vast territorial dimensions and complex energy mix, its carbon emission factors exhibit regional disparities. According to the most recently published carbon emission factors of the six regional power grids by the National Development and Reform Commission (NDRC), North China’s power grid is dominated by thermal power, and it has the highest carbon emission factor of 0.8843 kg CO2 /kWh of electricity; Central China’s power grid has a large amount of hydropower, and it has the lowest carbon emission factor of 0.7035 kg CO2 /kWh of electricity. It is important to note that carbon emission factors are subject to change in line with alterations in a country’s energy composition. For accurate carbon emission calculations, it is imperative to adhere to the most recently published carbon emission factors. Moreover, the evaluation of low-carbon buildings, communities, and cities should not be solely predicated on the absolute value of carbon emissions. It is essential to incorporate regional policies and regulations as well as concerted efforts to reduce energy consumption and emissions into the assessment. This multi-dimensional approach ensures a more accurate and

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contextual understanding of carbon emissions and the effectiveness of low-carbon initiatives.

3.5 Carbon Emission Characterisation The assessment of carbon emissions primarily encompasses three stages of characterisation, namely emissions per unit of GDP, emissions per capita, and emissions per unit of geographical area. Each stage sheds light on different facets of a country’s development and its carbon emissions profile. ● Emissions per Unit of GDP: This measure, often referred to as carbon intensity, indicates how much carbon dioxide is emitted for each unit of economic output (GDP). It is an important indicator for understanding the relationship between economic development and environmental impact. As countries develop and optimize their industries, they usually find more efficient and cleaner ways to produce goods and services, which results in lower carbon emissions per unit of GDP. ● Emissions per Capita: This measure reflects the average carbon emissions attributed to each individual in a country. Developed countries with higher living standards usually have higher per capita emissions because of greater consumption levels and energy use. It is essential for countries to reduce emissions per capita to lower their overall carbon footprint. ● Emissions per Unit of Geographical Area: This measure examines the carbon emissions concerning the land area of a country. It helps in understanding the density of emissions and can be particularly useful for analyzing emissions in countries with large land areas. It is noteworthy that most developed countries have advanced through the first two stages, and some have even peaked in the third stage and are transitioning towards carbon neutrality. However, it is essential to understand that carbon dioxide lingers in the atmosphere for around 200 years, and the emissions from the industrialization era in developed countries significantly contribute to the current global warming. In the case of China, it has made progress through the first stage by peaking its carbon emissions per unit of GDP. This reflects an improvement in the efficiency of its economy with respect to carbon emissions. However, China is still classified as a developing country with a large population and burgeoning economic growth. As such, it would be inequitable to immediately expect China to peak in terms of emissions per capita or per unit of geographical area, which are milestones that typically coincide with a higher stage of development. It is crucial to recognise the developmental context and historical emissions in framing global carbon reduction targets. For China, these three indicators should serve as developmental guides in the formulation of policies and regulations. A phased and balanced approach that considers China’s unique developmental challenges, and global historical emissions,

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can facilitate a more equitable and effective path towards reducing carbon emissions and contributing to global climate goals.

3.6 Carbon Trading and Carbon Quotas Carbon trading and carbon quotas are important measures for achieving carbon peaking and carbon neutrality. We need to be clear about how carbon quotas are set, as simply converting building energy consumption into electricity consumption can lead to errors and disputes. That is because among the main energy sources for buildings, coal, oil and natural gas are all primary energy sources with relatively stable carbon emissions per unit; while electricity is a secondary energy source. Due to the complex energy structure of China, its carbon emissions vary greatly from region to region and from time to time. In practice, there is still some debate regarding how carbon quotas should be made. The authors suggest that total CO2 emissions of a building should be calculated separately for coal, oil, gas and electricity costs, and then summed up to evaluate the real carbon footprint.

4 Conclusions This work has examined the crucial aspects of carbon emissions, with a particular focus on China. It underscored China’s commitment to achieving carbon neutrality by 2060 and the challenges therein, considering the country’s diverse energy sources. It is evident that a multi-dimensional approach is essential for effectively addressing carbon emissions and realising the shared objective of carbon neutrality. Energy efficiency and carbon mitigation in the building sector play an important role in China’s endeavours to achieve carbon peaking and, eventually, carbon neutrality. The essence of energy efficiency in buildings is to reduce energy consumption in the operation phase. Through the use of a range of active and passive measures, the operational energy can be reduced to low, ultra-low or even near-zero energy levels. Furthermore, the advancement of smart cities, the establishment of energy monitoring platforms, and the integration of carbon trading and quota systems will also help achieve carbon peaking and carbon neutrality. In summary, China’s path towards carbon neutrality is complex and demands comprehensive strategies that encompass accurate emission calculations, an understanding of regional factors, and the effective implementation of carbon trading and quotas. Collaborative efforts on a global scale are indispensable for supporting China and other nations in achieving sustainability goals, ultimately contributing to a more sustainable planet for generations to come.

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References 1. Archer D, Eby M, Brovkin V et al (2009) atmospheric lifetime of fossil fuel carbon dioxide. Annu Rev Earth Planet Sci 37:117–134. https://doi.org/10.1146/annurev.earth.031208.100206 2. Cheng J, Tong D, Zhang Q, et al (2021) Pathways of China’s PM2.5 air quality 2015–2060 in the context of carbon neutrality. Natl Sci Rev 8:nwab078. https://doi.org/10.1093/nsr/nwa b078 3. China’s State Council (2021) State council outline new development concept for carbon goals. In: Website of the State Council, PRC. http://www.gov.cn/zhengce/2021-10/24/content_5644 613.htm. Accessed 11 Apr 2023 4. China’s State Council (2021) White paper on China’s policies and actions in response to climate change. China State Council 5. China’s State Council (2022) National PM2.5 levels drop 34.8% in seven years in major cities. In: Website of the State Council. http://www.gov.cn/xinwen/2022-06/07/content_5694 356.htm. Accessed 12 Sep 2022 6. Dixit MK, Fernández-Solís JL, Lavy S, Culp CH (2010) Identification of parameters for embodied energy measurement: a literature review. Energy Build 42:1238–1247. https://doi. org/10.1016/j.enbuild.2010.02.016 7. Friedlingstein P, Jones MW, O’Sullivan M et al (2022) Global carbon budget 2021. Earth Syst Sci Data 14:1917–2005. https://doi.org/10.5194/essd-14-1917-2022 8. Hoegh-Guldberg O, Jacob D, Bindi M, et al (2018) Impacts of 1.5 °C global warming on natural and human systems 9. IMF (2022) World economic outlook 2022. International Monetary Fund 10. Jiang H, Guo H, Sun Z et al (2022) Projections of urban built-up area expansion and urbanization sustainability in China’s cities through 2030. J Clean Prod 367:133086. https://doi.org/10.1016/ j.jclepro.2022.133086 11. Masson-Delmotte V, Pörtner H-O, Skea J, et al (2018) An IPCC special report on the impacts of global warming of 1.5 °C. World Meteorological Organization 12. Röck M, Saade MRM, Balouktsi M et al (2020) Embodied GHG emissions of buildings—the hidden challenge for effective climate change mitigation. Appl Energy 258:114107. https:// doi.org/10.1016/j.apenergy.2019.114107 13. UNFCCC (1992) United Nations framework convention on climate change. https://unfccc.int/ resource/ccsites/zimbab/conven/text/art01.htm. Accessed 29 Jun 2023 14. United Nations Climate Change (2021) COP26: update to the NDC synthesis report | UNFCCC. https://unfccc.int/news/cop26-update-to-the-ndc-synthesis-report. Accessed 8 Sep 2022 15. Zhang S, Wang K, Yang X, Xu W (2021) Research on carbon peaking and carbon neutrality emission control targets in the building sector . Build Sci 37:189–198. https://doi.org/10.13614/j.cnki.11-1962/tu.2021.08.25

Exploring the Emergence of Digital Twins in the Construction Industry Jingwen Sun, Tongyu Zhou, Thushini Mendis, and Isaac Lun

Abstract In the face of increasing digitalization, this chapter surveys the emerging application of digital twins in the construction industry. Despite the construction industry’s traditional nature, the past decade has witnessed an increased emphasis on digital technology. However, the incorporation of digital twins within this sector remains in the early stages. This chapter provides a comprehensive review of its current usage and future potential. A primary focus of this review is the comparison between digital twins and building information modelling, highlighting the unique characteristics and overlapping features of each. Furthermore, the study delves into the influence of digital twins throughout the various stages of the construction process, from design to demolition. By providing an in-depth exploration of this cutting-edge technology, the paper aims to enhance industry development, promote sustainable practices, and offer valuable insights that will underpin future research in this evolving field. Keywords Digital twins · BIM · Construction life cycle · Literature review

1 Introduction 1.1 Research Background In today’s digital landscape, various software and equipment generate massive amounts of data across production and manufacturing processes. However, a lack of integration limits data utilization and efficiency [45]. Therefore, the use of digital twins (DT), a concept which has grown popular with the advancement of information technology, can harness this data more effectively [51].

J. Sun · T. Zhou (B) · T. Mendis · I. Lun Department of Architecture and Built Environment, University of Nottingham Ningbo China, Ningbo, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 T. Zhou et al. (eds.), Smart Buildings and Technologies for Sustainable Cities in China, Urban Sustainability, https://doi.org/10.1007/978-981-99-6391-1_3

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Originating from NASA’s Apollo space program, the concept of digital twins represented identical space vehicles on Earth and in aerospace for operational simulations [45]. Hernandez and Hernandez [25] later used it in urban road network design studies. Defined by [21] as a digital equivalent to physical entities, and by [26] as product avatars for bidirectional information flow, the term digital twins gradually replaced the concept of product avatar. Figure 1 shows the concept of digital twins that is generally accepted. With the rapid development of information technology, the application of computer-aided technologies, artificial intelligence (AI), the Internet of Things (IoT), and big data has broadened from manufacturing industries to diverse sectors, including construction [52]. This expansion has unlocked significant potential for the application and development of digital twins in various fields. Recognized globally for its significant contributions to economic growth, the construction industry accounts for an average of 8 to 10% of the GDP of various countries. This industry not only provides employment for many people but also spurs growth in other sectors through the purchase of raw materials [5]. However, the industry faces long-standing challenges such as low productivity, safety risks, raw material waste, and employee health concerns. Considering the construction industry’s pivotal role in the economy, fostering its sustainable development is a pressing matter. Harnessing the potential of technological advancements like digital twins could offer solutions to these persistent problems [39]. Fig. 1 Concept map of digital twin [10]

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1.2 Research Focus The construction industry, despite its traditional nature, has begun to place importance on digital technology in its development over the past decade, represented particularly by the rise of building information modelling (BIM). BIM and digital twins have similarities yet also contain distinctive characteristics. However, the integration of digital twins into the construction industry is still in its early phases, and research in this area is relatively sparse as compared to that of its application in the manufacturing industry. As such, conducting a literature review on the application of digital twin technology in the construction industry is crucial to enhance our understanding of its role in industrial development and to provide insights for future studies. This review will start by discussing the relationship between BIM and digital twins, followed by a summary of the usage of digital twins throughout various stages of the construction process.

2 Relationship Between Digital Twins and Building Information Modelling BIM is recognized for its ability to support the application of digital twins in the construction industry. Menassa [36] defined BIM as a process employed to create and manage a digital representation containing comprehensive information related to certain assets This defining characteristic makes BIM a strong candidate for early stage project deployment, primarily due to its capability of storing detailed and exhaustive information useful for building exploitation. On the other hand, digital twins serve as advanced counterparts to BIM, focusing on providing real-time data transmission. The convergence of these two aspects-BIM and digital twins-has been a significant topic in recent research, with most studies underscoring the benefits of leveraging BIM to develop a digital twin model. The advantages of integrating BIM to construct a digital twin model can be divided into three broad categories.

2.1 Generation of DT using BIM The potential of BIM in generating a DT model serves as a significant step forward in modern construction practices. As [48] have suggested, BIM’s capacity to render building components in 3D during the design phase presents opportunities for a more thorough and dynamic examination of proposed structures A demonstration of this was presented by [46], who employed BIM to construct a cable bridge model. The primary objective of this exercise was to imitate the bridge’s geometric features to facilitate an in-depth examination and analysis of its performance and its impact on the surrounding urban environment.

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Additionally, BIM can provide ontological data, which is essential to conduct simulations for the initial optimization of exploitation and construction processes, especially in the areas of carbon emission and energy consumption. For instance, [28] recommended using smart sensors to collect genuine data, thus enhancing the reliability of the DT model and promoting a better representation of the relevant environment. Similarly, an experiment by [3] involved installing smart sensors at key points within the Milan Cathedral to assess its structural health conditions. The application of analytical algorithms to process the collected data and identify correlations among these corresponding factors revealed a close relationship between BIM and the fundamental definition of digital twins, showcasing the evolution of traditional architectural practices into a digital era.

2.2 Life Cycle Management by Integrating BIM and DT In the ongoing exploration of improving life cycle management in the construction industry, the integration of BIM with DT models offers a promising avenue. Bolshakov et al. [8] conducted a study that underscores the importance of incorporating actors associated with a system’s operation and management during the design stage. They discovered that by doing so, a test operation can be established as early as possible. This approach plays a critical role in avoiding costly modifications in the future. Furthermore, the data collected can be used during construction to detect potential misalignments among various building elements by comparing them with the constructed elements. Such a strategy could potentially prevent future repair requirements [38], contributing to a more streamlined building life cycle. Extending this line of thought, [42] proposed that a BIM-based digital twin could be used to enhance existing operations within the building life cycle, including optimizing equipment usage and managing maintenance costs. These studies demonstrate the potential of integrating BIM with DT models in life cycle management. The possibilities range from early detection of design misalignments to optimization of operations.

2.3 Improvement in Data Management Data management is a critical facet of any construction project, and the advent of BIM-based digital twin technology presents potential improvements in this area. Jakobi et al. [27] proposed that a BIM-based digital twin can centralize data access for various stakeholders involved in corresponding projects. A practical demonstration of this centralization can be seen in the work of [40], who emphasized its critical role in airport management. By centralizing all relevant information, stakeholders can ensure data consistency, facilitating smoother operations and communication. Centralized data can also provide specialized knowledge and expertise for future

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analyses. In addition, the application of BIM brings added clarity and accessibility to the data. As [50] suggested, the 3D representation of physical objects provided by BIM creates a more tangible and intuitive way for stakeholders to understand and interact with project data.

3 Summary Considering the findings from previous research, the relationship between BIM and digital twins can be summarized as follows: ● BIM can contribute to the early optimization of the building life cycle management when creating a digital twin for any project. ● The digital twin can be viewed as an advanced version of BIM that provides realtime data transmission, while BIM can supply the geometric data for the digital twin model. ● Better decision-making and operation of any digital twin model can be achieved by integrating it with BIM.

4 Application of Digital Twins in Construction Life Cycle Looking at the specific elements encompassed within the entire life cycle of the construction industry, it is evident that current studies concentrate mainly on the design and engineering phase, the construction phase, and the operation and maintenance phase. The key research findings in these phases are as follows.

4.1 Scheduling Existing research concerning the use of digital twins during the scheduling phase is closely related to the dynamic elements and construction materials within this phase [1]. Several studies have centred on implementing lean techniques for construction scheduling, aimed at enhancing the efficiency of the modelling process inspired by digital twin technologies [2]. However, there remains a need for further investigation into scheduling models. Specifically, the relationship between scheduled components and monitored components warrants a more detailed exploration. This would ensure a comprehensive understanding of how digital twins can further streamline the scheduling phase.

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4.2 Construction Simulation The automation of the construction simulation process, harnessing various information technologies, has become a research trend. Accurately animating the construction sequence is considered the primary research area for digital twins related to construction simulation [7]. Within the digital twin framework, advanced information technologies contribute to more precise estimations regarding project duration, working hours, and resources, involving 3D modelling and BIM. However, given the various data sources, much information still needs to be input manually. Consequently, it is evident that current automation techniques compatible with digital twins are somewhat limited and require further exploration [11].

4.3 Clash Detection DT has demonstrated their efficacy in identifying conflicts in construction projects through 4D modelling. Enders [15] identified three key areas where clash detection is particularly useful: scheduling conflicts, resource and cost conflict analysis, and site conflict analysis. In addition to this, [17] offered an innovative approach for conducting clash detection with respect to the workspace. They demonstrated how digital twins can enhance the monitoring of temporary sites and objects, as well as the interactions among them. The use of digital twins in this context can offer a level of insight that is typically hard to achieve with other methods, thereby improving the management of potential workspace clashes. Gabor [17] presented a method to conduct clash detection related to the workspace using digital twins, which enables effective monitoring of temporary sites and objects and the interactions between them. It was discovered that 4D simulations are most commonly used for communication objectives with clients regarding planning and site logistics [18].

4.4 Safety Management In the field of construction, safety management is a critical concern and digital twins have shown great promise in enhancing this aspect. Kim et al. [29] suggested a method that combined various construction sequences to pinpoint potential hazards, specifically those associated with scaffolding. This methodology demonstrates how the integration of digital twins can significantly enhance safety planning related to built structures, thereby raising the quality and efficiency of safety management practices [19]. However, there are some limitations to this approach. Notably, the current digital models based on digital twins sometimes fall short of accurately predicting safety issues within complex spatiotemporal contexts. These are often subtle risks that are

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easily overlooked and omitted, presenting a clear area for further development and refinement. In a related study, [44] conducted research on construction site safety, taking spatiotemporal collisions as their main evaluation criteria. Their findings highlighted the risk factors associated with the application of digital twins. Despite the identified risks, the study underscored the utility of digital twins in enhancing site health and safety. The digital models were particularly effective in visualizing and detecting hazards in advance, thus allowing for preventative measures to be implemented. Extending this concept to high-risk scenarios, [6] effectively used digital twin models to identify dangers when operating at higher altitudes. Their findings lend further support to the argument that digital twins can play a crucial role in improving safety standards and outcomes in the construction industry.

4.5 Cost Estimation Cost estimation is a crucial part of construction planning and management, and digital twins offer innovative solutions in this realm. Several studies have, however, identified various obstacles to the full-scale implementation of digital twins within the construction industry. These studies have proposed potential solutions, such as the development of decision-making frameworks and improved building estimation mechanisms, which would apply to the entire life cycle of the construction process [20]. These recommendations are designed to optimize the utilization of digital twins and overcome current limitations. The prevailing approach is to link digital twin technologies with cost databases. This linkage aimed to provide accurate and up-to-date cost estimations for entire project life cycles, aiding efficient cost management. However, despite the potential benefits, this integration has proven to be a complex task. The primary challenge lies in the lack of interoperability, which is the ability of systems to work together within a large scale framework. This limitation hinders the effective handling of comprehensive data needed for robust cost estimations. Addressing this interoperability issue is therefore crucial for harnessing the full potential of digital twins in construction cost estimation and management. The evolution of digital twins in construction cost estimation will require continued research and development to overcome these challenges.

4.6 Design and Engineering Phase Digital twins, through the mechanism of interaction optimization, can fuse the information model and the actual physical model. This fusion is effective in reducing design time and production expenditures [31]. The design phase typically consists of four steps: task identification, concept design, embodiment design, and detailed

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design. Incorporating digital twins into these steps can enhance data integrity, product traceability, and knowledge accessibility, as suggested by [41]. An interesting comparison of digital twins was offered by [49], who likened them to the intuitive skill of a village cobbler, dynamically reflecting customer demands and design constraints. Building on this metaphor, [47] suggested that designers could create a detailed digital footprint of the building design process using digital twins. In this manner, abstract data can be transformed into tangible information, thereby facilitating timely and informed decision-making. In terms of reconciling design intent with end-user needs, [43] proposed that digital twin technology can check for conformance between a designer’s intentions and a consumer’s demands regarding building specifications. The result is a comprehensive reference model that best serves all parties involved. Grieves [22] pointed out another advantage of digital twins: they allow for interactive optimization between the virtual and the physical model, leading to reduced expenses during the design stage and unanticipated cost savings. Key tasks in the design and engineering stage, such as inception, briefing, designing, and engineering [24], can be facilitated by the application of digital twins. Nevertheless, there remains a challenge as highlighted by [33]. They noted a gap between existing buildings and their geometric digital twins and proposed a slicing-based object method to create the geometric digital twin of a concrete bridge. This research underlines the ongoing need to refine the processes of digitization and modelling to ensure accurate representations of physical structures. The continued development in this area signifies the potential of digital twins to revolutionize the design and engineering phase of construction projects.

4.7 Construction Phase During the construction phase, a variety of raw materials are integrated and transformed into buildings. As customization demands soar, companies must respond more accurately and promptly to client needs. In this context, the concept of smart construction is emerging. Digital twins facilitate interaction between the digital and physical worlds in a closed-loop environment, representing the core value of digital twins [9]. Grieves [23] suggested that the actual building process can be mirrored digitally, by comparing virtual buildings with their physical counterparts. This approach ensures that the construction adheres to the design specifications. Applying digital twins can also make construction factors context-aware, allowing for intelligent decision-making through communication with the surrounding environment [13, 14]. In assessing the integrity within construction systems, especially of historical buildings, digital twins have proven invaluable. For instance, [3] utilized a digital twin simulation model of a historical building to gain a clearer understanding of structural actions across various construction stages. Additionally, digital twin models can

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be continuously updated using data derived from different components of a historical building. This dynamic updating process is crucial, especially when information about existing damages, such as those collected from structures like the Milan Cathedral, is essential for initiating restoration work [37]. In situations where design drawings cannot be materialized, digital twins can be employed during the construction phase to prepare as-built drawings [35]. As-built models provide critical information for analyzing specific elements within existing structures. However, most situations currently employ reactive controls, resulting in discrepancies between expected and actual performances, and causing disruptions in the information flow among equipment, labour, and materials. This highlights the pressing need for a more proactive approach, one that leverages digital twin technology, to improve the efficiency and accuracy of the construction process.

4.8 Operation and Maintenance Phase During the operation and maintenance stage, constructors are no longer in control of the project, making continuous management and access to the project a complex task. Herein, the value of a virtual model based on digital twins, capable of creating connections with physical objects, improves the project’s maintenance [2]. It is at this stage that the relationship between constructors and residents often becomes disconnected, posing challenges in data management and access, and making a closed-loop data stream difficult to achieve [30]. Simultaneously, constructors and residents are increasingly concerned with the real-time operational status and maintenance strategies. Under these circumstances, digital twin applications enhance the understanding of degradation and anomalies and predict potential issues [12]. Digital twins can also help mitigate degradation or damage by initiating a self-healing system, as the high-fidelity model allows for dynamic prediction and adjustment [16]. This proactive approach to maintenance could potentially extend the lifespan of structures and reduce costs. Various stakeholders participate in the operation and maintenance process, each contributing different data sources. This diversity makes it more challenging for constructors to integrate information from different stakeholders at different phases. Implementing digital twin technology during operation and maintenance can optimize various aspects of management, including maintenance management, logistics processes, facilities management, and energy simulations related to the project. In this manner, digital twins empower managers with insights to make crucial decisions concerning project operation and maintenance, performance management, and energy consumption optimization. The benefits of implementing digital twin technology extend to enhancing operation efficiency, precise maintenance prediction, and more informed decision-making [28]. Further, digital twins can provide managers with a tool to analyze various potential scenarios, ultimately improving residents’

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living comfort and energy consumption [34]. This aspect underlines the transformative role of digital twins in ensuring sustainable and user-centric operation and maintenance practices.

4.9 Demolition and Retirement Phase Knowledge related to the retirement of buildings and systems often gets overlooked, leading to recurring problems in subsequent building and system generations. Applying digital twins involves recording the product and system lifecycle, including their retirement phases. Thus, previously occurring problems can be maintained virtually with reduced expenditure, crucial for preventing similar issues in the development of future product and system generations [23]. For example, [32] studied the uncertainties in remanufacturing and construction processes using digital twin technologies. However, according to the literature review of current studies, research applying digital twins to the retirement phase is limited. In the construction industry, the retirement phase also encompasses the demolition and recovery stages. During this stage, the performance information of buildings often gets overlooked. Grieves et al. [23] suggest that information collected during the demolition and recovery stage plays an important role in future construction as it can prevent similar issues in subsequent building generations. An important application of digital twins at this stage is the preservation and conservation of heritage assets, which are vulnerable to damage due to their historical significance. Several projects have used Historic Building Information Modeling (HBIM) to improve the collaborative exchange of information related to existing constructions [4]. Table 1 below summarizes the keywords associated with the applications of digital twins in the construction industry. Table 1 Keywords related to the application of digital twins in the construction industry

Keywords

Design and engineering phase

Construction phase

Operation and maintenance phase

Demolition and recovery phase

● Interactive optimization ● Data integrity ● Virtual assessment ● Virtual verification

● Real-time monitoring ● Construction control ● Asset management ● Human–robot collaboration ● Performance prediction ● Process assessment

● Maintenance prediction ● Fault detection ● State monitoring ● Virtual test

● Yet to be explored

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5 Conclusions This research provided a literature review of digital twin technology in the construction industry, exploring its concept, relation to BIM, and applications throughout the construction life cycle. Despite strides made in establishing a framework for digital twin use in construction, there remain gaps in research and practices. It is important to establish a clear and unified definition to help set standards for the widespread application of digital twin technology in the construction industry. Moreover, further exploration is needed regarding the differences between digital twin technology and BIM. While existing studies mainly look at how BIM benefits early project stages to develop digital twin models, there is a lack of comparative research on the two techniques. Furthermore, despite identifying key applications across various phases of construction (design, engineering, construction, and maintenance), research on digital twin usage in the construction industry remains limited compared to other sectors, such as manufacturing. Few studies focus on the demolition and recovery phase, indicating a potential area for future investigation. The research implications are two-fold. It offers construction industry practitioners insights into the potential applications of digital twin technologies across different project stages. Moreover, it identifies the relatively early stage of digital twin application in construction compared to other industries, suggesting a need for future research to assess the industry’s readiness for wider adoption. In conclusion, this review highlights the need for unified definitions, comparative studies with BIM, and expanded research into the use of digital twins throughout the construction life cycle, including the demolition and recovery phases. These findings can inform both theoretical and practical efforts towards the wider application of digital twin technology in the construction industry.

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Data Anonymization and Open Sharing Are Key to a Sustainable Built Environment Fazel Khayatian

Abstract Research on building energy systems is shifting toward data-driven modelling due to the complications of setting up physics-based models. Datadriven learning from buildings faces an important challenge, namely, accessibility to input data for training the models. The scarcity of building energy data is mainly derived from privacy concerns, which are of greater importance for residential buildings. Restrictions imposed on information prevent researchers from sharing data for reproduction and benchmarking purposes and impede comparison between different models. Therefore, there is a clear void concerning open datasets of building energy consumption, which may impair the advancement of machine learning and data mining for building energy research. One viable solution to overcome the barrier for open data sharing is through data anonymization. Recent advancements in machine learning have introduced reliable models that suitably anonymize data with little information loss and high security. However, the building research community still face a number of challenges that are vital for the safe and reliable dissemination of data in an open manner. This chapter introduces the hurdles, describes the most promising methods, and provides a synthesis of the next steps to overcome the existing barriers to open data sharing in the building research community. Keyword Open data · Building performance · Data privacy · Anonymization · Synthetic data

1 Introduction The well-documented impact of the construction sector on the climate change [62] has boosted global efforts to curb buildings’ share in global energy consumption [19]. The outcomes are standards, regulations, and directives for a wide range of actors who are involved in the planning, design, and operation of buildings. A F. Khayatian (B) Urban Energy Systems Laboratory, Empa-Swiss Federal Laboratories for Materials Science and Technology, Dübendorf, Switzerland e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 T. Zhou et al. (eds.), Smart Buildings and Technologies for Sustainable Cities in China, Urban Sustainability, https://doi.org/10.1007/978-981-99-6391-1_4

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side-outcome is physics-based modelling and simulation tools to quantify buildings’ energy consumption [9]. These tools are also utilized to explore energy/cost-optimal solutions and improve buildings’ energy performance through interventions. As a result, building energy modelling and optimisation is a common practice (and in some cases a mandate), the results of which often form national and global energy saving projections. However, it is not uncommon that the estimations of energy consumption or the projected energy savings differ from reality [9]. This discrepancy, commonly known as the building performance gap, is stemmed from uncertainties associated with the model’s inputs. Frequently, incomplete knowledge of buildings’ characteristics forms weak prior assumptions that are fed into simulation tools and return misleading results [76]. Consequently, it is necessary to measure the reliability of building energy models by contrasting simulation outputs against the actual energy consumption. This process not only helps to validate the accuracy of predictions but also enables the calibration of the model inputs. Namely, refining the assumptions about building characteristics such that the simulation outputs match the measurements. Building energy calibration initially started with monthly bills, but incrementally resorting to hourly measurements became the common practice. As the temporal resolution of the measurements increased and more data became available, the calibration process became excessively cumbersome, often requiring hundreds to thousands of building performance simulations. Building performance calibration also became much more complex, as the dimension of the measurements increased and co-dependencies emerged. The following sections in this chapter discuss the urgent need for reproducible research in building performance; map the bottlenecks for promoting reproducible research in the field; and explores solutions that may help overcome the hurdles of open data sharing, i.e., challenges which primarily stem from data privacy concerns.

2 Building Performance Research and Reproducibility The challenge of reproducible research in building performance research was a minor issue during the building simulation area, as models were physics-based and equations were shared and published without restrictions. The struggle to accurately calibrate building energy models with (sub) hourly measurements coincided with the resurgence of AI (Artificial Intelligence) and the uptake of machine learning (ML). ML offers comparable or better predictions, with minimal or no manual programming. Therefore, the idea of a “black-box” (machine learning-based) model could be appealing for building energy scientists, as it eliminates the need for labor-intensive physics-based modelling and calibration. This approach not only increases the accuracy of predictions but also greatly reduces the computation burden. Consequently, building energy researchers are shifting to ML-based solutions that can predict building energy consumption with high accuracy, regardless of the granularity of the temporal resolution. A task that is otherwise unfeasible through conventional

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physics-based building energy models. On the other hand, machine learning models are commonly at the risk of exposure to biased datasets and prone to overfitting. As recent as 2020, scientists still emphasize the importance of reproducible research [20]. In this chapter, reproducible research in the field of building performance, refers to a specific type of research in which, the outcomes of the study can be recreated by following the descriptions that are provided in a research report. For a research to be reproducible, it is imperative that the researchers openly dissemination the research data and methods alongside the research report. Hence, developing models with data that are subject to access restrictions continues to be the major challenge for reproducible research [47]. Therefore, it is crucial to promote reproducibility—especially in ML research with is heavily reliant on inputs—by providing a suitable infrastructure for sharing data and models. This would help in gaining confidence in the developed machine learning models, including the declared performances in terms of accuracy and generalizability. Furthermore, researchers should continuously evaluate the accuracy of their models beyond the case-specific data and update the reported performance of their models accordingly as fresh sets of data emerge.

3 The Challenges of Reproducibility in Building Performance Research Reproducing and validating models is challenging within the context of building energy research. This particularly applies to ML-based solutions, which are highly reliant on long-term measurements. Lack of adequate and suitable open datasets constrain researchers to opt for private data when training data-driven building energy models [49]. This attitude of case-specific model development hinders proper benchmarking of new models. Studies continue to stress the urgent need for open datasets of building performance and consistently underline the drawbacks of using isolated case studies for training and validating machine learning models (Kathirgamanathan et al. 2021). Therefore, it is imperative to establish a pathway through which every research on building energy analytics—particularly with a focus on ML—can be validated and reproduced. This argument is specifically based on the notion that “Currently, there are still many barriers on the integration and sharing of energy big data … [and] lack of accessible data hampers researchers that are working on big data and smart energy management” [75, p. 222]. This urgent need for accessible data is reinforced by the proven success of open data sharing, as demonstrated by the AI community [10]. Consequently, it is imperative to lay the foundations for open and free sharing of building performance data. The outputs of such an initiative will be immediately useful for building energy researchers who seek to validate their machine learning models on comparable datasets and benchmark their performance against other models. Open sharing is also the first step toward a standardized, comprehensive, and domain-specific dataset, which would form the foundations for

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systematic knowledge transfer from the bleeding edge of ML to building energy performance. It is also believed that open data sharing will aid the development of new metrics that are tailored to evaluating and benchmarking ML-based building energy models.

4 Open Data is Essential for Benchmarking ML-Models Innovation in ML is tightly associated with the availability of data. The ML community realized the urgent need for large-scale data as early as the 1980s. ML scientists tackled the challenge by promoting the open sharing of data, which significantly accelerated in the twenty-first century. As a result, every advancement in ML research is benchmarked on a list of open databases. Building energy researchers also noticed the importance of benchmarking models through open data. Progress has been made in recent years by introducing benchmarking data from a variety of sources, spanning from occupant behaviour [41] to building appliances [30] and ventilation systems [63]. Datasets are complemented by energy consumption data from both residential [55] and commercial buildings [58]. However, despite the considerable efforts from the building research community, the uptake of open datasets for research purposes has been suboptimal [18]. This shortcoming cannot be attributed to the scarcity of data as 81% of all ML-based building energy research opt for real measurements, whilst only 14% resort to open data [1]. The lack of enthusiasm for using open data is because no single dataset is suitable for benchmarking all ML models. Open datasets of building performance are often limited to a handful of buildings or climates, which would impede large-scale benchmarking [61]. In addition, most open datasets contain daily or monthly aggregated energy consumption values [3, 6, 45] or are limited to a few weeks of observations [34], which renders them unsuitable for in-depth energy analyses. Few open datasets do cover a full year with (sub)hourly measurements, however, lack some vital features (e.g., indoor air temperature) [7, 36, 43, 51–53], which would rule them out for energy flexibility studies or research on model predictive control. Contrasting the few open datasets of building performance against the numerous publications with private data hints at a strong barrier to open and free sharing of building measurements. Dealing with issues related to data scarcity is not limited to researchers of the built environment, but also the entire research community. The research community’s effort to seek research data that is not findable, accessible, interoperable, and reusable costs the EU 4.5 bn per year [12]. Therefore, research communities around the world are actively promoting the open sharing of data. For instance, the EU has created a dedicated portal that hosts more than 15,000 datasets [13]. The US DOE has three separate programs dedicated to open building data with a budget of over $4M [70]. Using open data to promote renewable energy technologies and the consequent effects on energy savings has also been well documented [26]. Furthermore, studies have shown that linking a data repository to a publication increases the citations by 25% on average [8]. This has incentivised many journals to enable

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open data sharing and encourage linking the article to a permanent repository, whilst other journals have made open data mandatory for publication. Furthermore, data availability statements are not very useful. A survey of 1792 papers whose authors declared datasets are available on reasonable request revealed that 1669 (93%) did not respond or declined to share their data [14]. Finally, it has also been shown that a journal’s mandate for open data sharing does not necessarily guarantee the reusability of the data [22]. Therefore, much work is required to ensure that the provided infrastructure for sharing data is utilized in an optimal manner, i.e., reproducibility goes beyond sharing data, but also procedures for reproducing the research results. This challenge starts with defining metrics and thresholds that define what features constitute a useable, anonymous, and open dataset.

5 Privacy is the Bottleneck, Anonymization is the Accelerant The most important barrier to free and open sharing of building performance data can be attributed to fears over invading users’ privacy [37]. This is also evident from the small share of residential buildings in the open datasets [53], where privacy concerns peak. Similarly, it explains the larger availability of open data and greater progress in benchmarking commercial buildings [15–17]. Concerns over the open sharing of data stem from the fact that building performance datasets contain sensitive information about the everyday life of the occupants. Mining these data can reveal private information and expose patterns of occupancy [66], which potentially could be misused to adversely affect the resident. For instance, patterns of occupancy could be used for burglaries and stalking [40], targeted advertising [4], insurance and landlord-tenant disputes [2], custody battles [57], or members of a dwelling monitoring the activity of other members [23]. As the temporal resolution of measurements increases, the level of details revealed through measurements surges proportionately [46]. This explains why open datasets of building performance with fine (e.g., minutely) temporal resolutions are extremely rare and limited to either a single building or a short observation period [29]. There are also monetised incentives that can impede the open sharing of data and potentially work as a competitor to open-source research. For instance, the largest dataset of metered building performance is not openly available [56], yet frequently used for developing models [39] and frameworks [42]. One approach to overcoming data privacy concerns and mask personal or sensitive information is through data anonymization [73]. The simplest approach to anonymization is through attribute masking or suppression (syntactic anonymity). In such cases, information about a building’s address and end-use is obfuscated to preserve the owners’/tenants’ privacy [53]. Open data release through attribute masking and suppression seems to be most fit for competition rather than research purposes [50, 51]. Generally, building scientists tend to rationalize the performance

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of their black-box solutions by looking into the building characteristics such as the geometry, construction, and surroundings. However, these features are not typically included in the aforementioned open datasets. This shortcoming casts a shadow on the trustworthiness of the ML-based solutions that are developed by building scientists and undermines the advantages of black-box models such as development speed and prediction accuracy. An alternative solution is to anonymize the measurements. Data anonymization can be performed by various techniques as shown in Fig. 1, among which generalization and perturbation are most suitable for time-series data [74]. A common issue with data anonymization through generalization methods is that information about an entry (e.g., a building) could become biased. Such modifications may affect the accuracy of predictions and analyses. Perturbation methods, on the other hand, have shown promising results in protecting users’ private information without suppressing and truncating the dataset, however, are much more difficult to implement. Brief descriptions of the two categories of data anonymization solutions are provided below. 1. Generalization is the process of removing details from a dataset. Generalization can be practiced through various methods such as spatial/temporal aggregation, grouping/clustering/binning, and averaging/smoothing. Generalization methods tend to overlook important details within a dataset, which often leads to oversimplified inferences about a building’s performance. 2. Perturbation is the process of carefully altering the values within a dataset without modifying the resolution or the range of peaks and valleys. Perturbation can be enforced through different methods such as infusing noise, or

Fig. 1 Approaches to preserving privacy in data

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creating synthetic datasets. The main challenge with perturbation is preserving correlations in multivariate datasets.

6 Precedence of Anonymizing Building Performance Datasets Anonymization can be divided into two categories based on the application: (1) realtime data anonymization that often deals with small batches of data and is tailored to energy metering or the operation of a smart grid (2) anonymization of large historical measurements for open dissemination purposes. The first category concerns data anonymization on the fly for recurring online queries from a raw dataset. Differential privacy falls under this category, where carefully crafted noise is added to entries of a query to mask the actual values while guaranteeing that the query—as a whole— preserves the statistical characteristics of the original dataset [32]. It is important to note that differential privacy is most suitable for datasets that are stored centrally and allow queries from a protected source, rather than datasets that are fully released to the public [68].The second category is applied to datasets that can be retrieved in whole with a single query. Such datasets are anonymized before being stored online. Given that this chapter focuses on the open sharing of building performance data, the emphasis is placed on the latter application, i.e., anonymization of large historical measurements (offline anonymization). Anonymization by means of generalization has been widely practiced for releasing building performance data. Most of the existing methods aggregate (sub)hourly measurements to monthly or annual values for benchmarking purposes [61]. Some datasets are also aggregated spatially to prevent insights into the daily routines of individual buildings [67]. That said, data aggregation should be tailored to the end user. Thus, either temporal or spatial aggregation can potentially mask information that is necessary for the application or distort the end-users’ understanding of the data. For instance, it is shown that temporal aggregation of buildings’ energy consumption can shift the peak values of indoor air temperature and energy consumption, and therefore, is unsuitable for in-depth energy analyses [31]. Anonymization by means of perturbation is inherently dependent on two important characteristics of a dataset. First, the number of members (i.e., buildings) in the dataset, and second, the dimensionality of the measurements—including the multivariate characteristics—of the dataset. In the following discussion, the chapter refers to several best practices that have perturbed datasets of building performance. While few of the following examples are specifically designed and implemented for data anonymization purposes, other methods without a focus on privacy can still be considered as a stepping stone for advancing anonymization practices in building performance research. The chapter will also discuss perturbation from two perspectives, i.e., (1) Noise infusion and (2) synthetic data projection.

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6.1 Noise Infusion Infusion of noise through randomized value distortion has been practiced to hide sensitive information of different attributes. This approach preserves the overall statistical characteristics of the underlying distribution of the data, despite modifying the values of individual samples. Thus, when mining the perturbed data, the extracted patterns are very similar to those of the original dataset [28]. However, noise infusion also has a number of drawbacks. In many cases, the noise can be separated from the original dataset [27]. Also, the addition of noise to confidential attributes could make a dataset susceptible to bias. Four types of biases are identified for data perturbation, i.e. (1) changes to the mean of the dataset, (2) changes to the relationships within confidential attributes, (3) changes to the relationship between confidential and non-confidential attributes, and (4) changes to the multivariate distribution characteristics of the dataset. Infusion of noise has been used for perturbing building performance profiles [21] but also, specifically, for preserving the privacy of electricity consumption of houses that are connected to smart grids [35].

6.2 Data Projection Generating synthetic data is an alternative to anonymization. Namely, composing a dataset that does not exist in reality but resembles actual measurements. Using synthetic data alleviates concerns over personal and sensitive information without severing or distorting the dataset. The application of synthetic data for building performance studies dates back to the late 1980s [48]. Incidentally, thirty years ago, the generalizability of regression models and their questionable reliability for benchmarking was the main driver for creating synthetic building performance data [60]. During the past few years, the idea of using synthetic building data is slowly recycled, once again citing insufficient data and privacy barriers for using real measurements [25]. Backed by the Swiss Federal Office of Energy, TEP Energy proved that creating a synthetic building stock in Switzerland is feasible and argued it has a great potential for large-scale benchmarking of the building energy consumption [54]. Similarly, the US Department of Energy supported the Lawrence Berkeley National Laboratory to initiate the “Energy Data Vault”. The project resorts to building energy models, as well as a mix of public and private data to create a synthetic database of building performance [71]. The idea of using synthetic data has also been up-scaled to an entire city, a practice that is particularly useful for designing decentralized energy systems [11]. The renewed interest in synthetic datasets of building performance has provided resources for a variety of applications, for instance, evaluating power distribution algorithms [44], non-intrusive load monitoring [33], occupancy inference [72], model calibration [64], and uncertainty propagation [59]. Synthetic data projection also has a number of drawbacks. The process can be labour-intensive when projecting synthetic data by open-box [38]. Most closed-box models—on the other

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hand—cannot provide a formal guarantee of anonymity, lack transparency on the data anonymization process, and may even leak sensitive information [24, 65, 69].

7 The Path Forward for Promoting Open Data of Building Performance The variety of projects for gathering and distributing building databases indicates a global effort for open and free sharing of data to advance building energy research. One successful example is the Building Data Genome project [50, 51], which expanded from two to eight partner universities in four years. However, the current attitude toward gathering and sharing open data is through one-off funded projects. These projects can only cover part of the requirements for composing a diverse and inclusive database, as they are limited by the budget and objectives of the task. On the other hand, in a world of heterogeneous and multidisciplinary research, every small chunk of data can potentially become a valuable contribution to a comprehensive dataset. Therefore, instead of creating one open dataset from a single source, it would be preferable to gather small contributions from a large and diverse group of suppliers. However, there are currently no levers to facilitate data sharing from different contributors. Namely, none of the existing anonymization platforms enables a hassle-free exchange of building measurements and performance data. This barrier is because all building anonymization methods are drafted for case-specific applications. Consequently, the anonymization methods are either too complicated for experts from other research fields or lack the flexibility to handle other types of data with dissimilar characteristics. Hence, it is no surprise that the building research community’s contribution to open data has been suboptimal. This lack of enthusiasm can be attributed to the practicality and applicability of the existing methods, including: 1. In-depth knowledge of a tool: All existing solutions for creating anonymized building performance data rely on a modelling platform (e.g. EnergyPlus, TRNSYS, Reference Network Model, etc.), and therefore, are tailored to a specific audience. 2. Suppressing building information: The solutions either completely ignore the role of building geometry and surroundings or represent them through archetypes, which heavily suppress vital information such as building orientation, window area, and the thermal conductivity of surfaces. 3. Standards or requirements: Studies do not discuss what characteristics would constitute a practical anonymized dataset. Specifically, a balance between preserving vital information within a dataset whilst preventing leakage of private and personal details. The outlook of open data dissemination for a sustainable built environment seems grim on the current trajectory. Several challenges must be addressed before open data sharing can become common practice:

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● Researchers of the built environment must work with peer scientists from statistics and machine learning communities and establish guidelines for anonymizing measurements from the built environment. Specifically, the guidelines should be tailored to applications that contribute to a sustainable built environment and categorized based on the modality of the data (table, time series, images, audio, etc.). ● Since data anonymization is a trade-off between privacy and loss of information, no single obfuscation solution will suit all applications. Therefore, there is a need for end-to-end pipelines that automate the process of data anonymization—from raw measurements to masked data—given a specific end-use. ● While the literature is rich with potential harms that can be inflicted from adversarial data mining, concrete examples of misusing open-datasets from the built environment are missing. This shortcoming should be addressed from both technical and social perspectives to help establish the proper key performance indicators (KPI) for data anonymization processes.

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Modeling of Building System Operational Faults for Improved Energy Efficiency Rongpeng Zhang, Yu Yang, and Chengkai Lin

Abstract Efficient building system operation is vital for achieving the energy and sustainability goals established during the building design phase. However, operational faults are prevalent in existing building heating, ventilating, and air conditioning (HVAC) systems of existing buildings. These faults often result in a significant discrepancy between actual HVAC operation performance and design expectations, leading to decreases in energy efficiency and occupant comfort. To address this issue, modeling and simulating technology can serve as an effective approach to evaluating and analyzing building operational faults. By adopting a holistic approach, this method can adequately account for the coupling between various operational components, the synchronized effect of simultaneous faults, and the dynamic nature of fault severity. Consequently, a deeper understanding of these faults can be attained, facilitating a more accurate estimation of their severity, and supporting timely decisionmaking for fault corrections. This chapter aims to explore the reasons behind the occurrence of representative building operational faults and the potential risks associated with them. Furthermore, it compares different fault detection and diagnostic methods developed for identifying and analyzing HVAC operational faults at the component or subsystem level. Additionally, the chapter introduces recent research and developments in the field of fault modeling and simulation tools. Keyword Intelligent building · Building system · Operational faults · Energy efficiency · Sustainable

R. Zhang (B) · Y. Yang · C. Lin School of Architecture and Planning, Hunan University, Changsha, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 T. Zhou et al. (eds.), Smart Buildings and Technologies for Sustainable Cities in China, Urban Sustainability, https://doi.org/10.1007/978-981-99-6391-1_5

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1 Operational Faults Widely Exist in Buildings 1.1 Operational Faults are Common in Existing Buildings The building sector contributes the largest portion of primary energy consumption in the world, exceeding both the industry and transportation sectors. Relevant statistics have shown that buildings (both commercial and residential) account for about 40% of the total primary energy consumption in the United States and Europe [4, 12]. This not only leads to enormous consumption of fossil fuel resources, but also produces severe environmental impacts like ozone layer depletion and global warming. The Heating, Ventilation and Air Conditioning (HVAC) systems account for the largest share (38%) of energy consumption in the building sector [36]. Hence, many researchers are devoted to developing more efficient and sustainable HVAC systems [41]. Some research has focused on energy sources, i.e., using renewable energy like solar or geothermal energy as an alternative driving energy source [2, 7, 41]. In addition, quite a number of studies have shown that the operational performance of HVAC systems can be improved by advanced scheduling [22, 23], which includes occupant behaviors detection [25], occupant thermal comfort personalized control strategy [30], etc. However, most buildings, especially those embedded with complex building energy systems, can have various degrees and types of operational problems. It is reported that the number of maintenance requests for building energy systems has increased exponentially throughout the past few decades, indicating an increase in building operational faults [26]. Therein, HVAC systems are usually subject to faults when put into the actual operation [8], since the systems have the characteristics of time-delay, strong thermal inertia, time-varying and high coupling. Several researchers have investigated the prevalence of HVAC system operational problems [9].

1.2 Potential Operational Faults of Building HVAC Systems The occurrence of failures is one of the important factors affecting the operation of HVAC systems [41]. Statistics on the operational condition of air conditioning units in residential buildings and commercial buildings in California indicate that faults exist in more than 65% of such units [13]. Common failures of HVAC systems mainly occur in three areas, that is: the control side, the sensor side, and the device side [14]. Performance degradation of devices in HVAC systems is also a kind of operational fault condition, which influences the indoor thermal comfort of occupants in buildings [41]. In addition, other factors like improper installation, sensor offset, or control logic problems also lead to HVAC systems operating in suboptimal or defective conditions. In general, typical operational faults of HVAC systems can be grouped into several categories, including: (1) control fault, (2) sensor offset, (3)

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Fig. 1 Potential HVAC operational faults of a VAV system in a central plant

equipment performance degradation, (4) fouling fault, (5) stuck component fault, and (6) others [3, 9, 38]. Taking the typical variable air volume (VAV) system in a central plant as an example, the typical operational failures are illustrated in Fig. 1.

2 Modeling and Analysis of Operational Faults in Building Performance There are many benefits to modeling and analyzing HVAC operating faults. On the one hand, modeling and simulating HVAC operational faults can lead to greater understanding by quantifying the impact of the faults on building energy use and occupant comfort. Modeling and simulation allow for an estimation of the severity of common faults and, thus, support decision making about timely fault corrections— which can then enable efficient system operation, improve indoor thermal comfort, reduce equipment downtime, and prolong equipment service life [10, 31, 32]. They can also support commissioning efforts by providing estimates for potential energy/ cost savings that could be achieved by fixing the faults during retro-commissioning. Quantified information on the impacts and priorities of various coexisting operational faults can be provided to the commissioners or the building management system, resulting in more reasonable and reliable commissioning decisions, especially when budget and staff resources are limited [31]. Moreover, modeling operational faults

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is critical to achieving more reliable energy model calibrations when most energy models for existing buildings assume ideal conditions without any operational problems. The ability to estimate the severity of common faults can be expected to improve the accuracy and transparency of the calibrated model, and therefore, increase the analytical accuracy of different retrofit measures [16, 19]. However, as building energy system configuration and control becomes increasingly complex, it presents several challenges for modeling and analyzing HVAC operational faults at the building level. In general, the following issues need to be considered and well addressed in a whole-building performance simulation to model and quantify the overall impacts of operational faults on the building.

2.1 Building Operational Faults Occur at Different System Levels The building energy system can be a sophisticated system with numerous interrelated equipment components that may present complex interrelation and coupling effects. Building operational faults can occur at different levels: component, subsystem, system, or even building level. A fault at any of these levels can further affect the operation of many other related components, and therefore, makes it difficult to understand the relationship between causes and effects and to quantify the overall impacts on the whole-building energy performance [6, 39]. For example, the degradation of fans may affect the air side of the system by reducing the airflow or increasing fan power. It may also affect the heat transfer performance of coils and their energy consumption, thus further affecting the water side performance of the system.

2.2 Diverse Impacts Presented by Operational Faults Operational faults may present diverse impacts on different aspects of the building performance at different times. For instance, a positive offset of the thermostat (i.e., the zone air temperature reading is higher than the actual value) can have varying effects on both energy consumption and thermal comfort during different seasonal periods. During the heating season, it reduces the heating energy consumption by maintaining the room temperature at lower levels, but, meanwhile, it deteriorates the indoor thermal comfort conditions. During the cooling season energy consumption increases, and over-cooling may occur. Investigations of these diverse impacts are essential to understanding overall fault impacts.

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2.3 Different Operational Characteristics of a Fault A particular fault may present very different operational characteristics and needs to be handled by taking a specific approach. Taking a temperature sensor offset as an example, it can be: (1) a static fault, if the offset is a constant value throughout the period of analysis; (2) an abrupt fault, if the offset arises suddenly during the period of analysis and stays at a constant level after occurrence; (3) a degradation fault, if the sensor offset drifts over time. These different cases need to be carefully distinguished and modeled using various methods which may use different features of the modeling tools. Some current research assumes abrupt and degradation faults to be static because of the limitations of modeling tool capacity [24], but this may oversimplify the problem.

2.4 Fault Modeling and the Characteristics of Existing Component Models Fault model design and implementation need to take the characteristics of existing component models into account in building energy simulation programs. Fault models are usually built upon and applied to specific existing component models. For instance, the condenser supply water sensor offset fault model is applied to the cooling tower model, because the fault leads to different cooling tower behaviors during faulty operation. The component model can be either physics-based or empirical. Physics-based component models have more operational parameters defined in the algorithm that can be directly manipulated by the fault model. This provides more flexibility for the fault model design and implementation. By contrast, an empirical component model is mainly based on the performance curves instead of first-principal physics, and therefore provides limited flexibility to apply the fault model. In this case, the fault model needs to be well designed to make use of the component model features and balance the model applicability and accuracy.

2.5 Fault Modeling and HVAC System Operational Modes Fault modeling and simulation tools should take various modes of HVAC system operations into account. For example, many faults may cause significant impacts during normal modes but little or no impact in free cooling modes. Operational faults may also need to be handled separately in different building simulation cases. For example, a thermostat or humidistat offset fault should only be introduced during a normal weather simulation case, but not during the sizing process, where maximum loads are calculated to determine the capacity of HVAC equipment. Since the offsets are unknown during the design phase, they should not affect the sizing of the

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system. Therefore, fault-modeling tools should have flexible and capable modeling capacities.

3 The Importance of Fault Detection and Diagnosis HVAC operation faults may lead to a considerable discrepancy between actual HVAC operation performance and design expectations [11, 18, 33, 37]. On the one hand, the HVAC systems are endowed with highly adaptive and self-correcting capabilities through the design stage, which makes them able to be fault-tolerant to a certain extent. For example, the operational faults can be compensated for by increasing the transmitted power, turning on more devices, or enlarging heat transfer temperature difference and some other measures. The superfluousness of this operation mode is difficult to perceive since the HVAC systems seem available. However, it will cause energy waste, and even gradually lead to equipment damage and bury security risks. It is estimated that poorly maintained and improperly controlled HVAC equipment is responsible for 15–30% of energy consumption in commercial buildings [3]. On the other hand, a series of questionnaire surveys and interviews show the significant influence of poor HVAC operation on occupant comfort. A number of maintenance factors are identified that are significantly correlated with the occupants’ satisfaction [1]. Therefore, fault detection and diagnostics are essential to ensure normal operation and reduce energy waste [8]. Relevant studies have shown that an energy saving of 5–30% can be achievable by applying fault detection diagnosis [21].

3.1 HVAC System Operational Fault Diagnosis In practical research, fault detection methods and diagnosis methods are often coupled together. Fault detection and diagnosis of HVAC systems refers to the process of analyzing the operating state of the systems by qualitative and quantitative methods, determining whether a fault occurs, and tracking the fault location. A variety of fault detection and diagnosis tools have been developed with various approaches, focusing on identifying and analyzing the HVAC operational problems. According to the dependence on the number of sensors and theoretical basis, the mainstream traditional HVAC fault detection and diagnosis methods can be divided into the following three categories. (1) Rule-based expert method This method is based on the physical meaning or physical relationship, to establish a set of rules describing the “state-reflection” conditional relationship based on characteristic variables and fault variables [35], which is mostly applied to equipment with a small number of sensors, such as air handling units and devices such as variable air volume terminals.

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(2) Model-based method This method involves establishing a series of indicators that are sensitive to specific faults and have physical significance, such as a physical model or black box model, and using the deviation between the expected indicator value and the actual indicator value of the equipment to detect and diagnose the fault. This method is mostly applied to equipment with a relatively large number of sensors, such as an air conditioning chiller. (3) Data-driven method Based on rigorous multivariate statistics [35], this method uses machine learning algorithms to extract the features of the historical data of normal operation or fault operation, and detects and diagnoses faults by judging the consistency of the current data with the features. Previous research has laid a solid foundation and provided a very promising development direction for further investigation. The data-driven method can fully mine the reflection relationship of related variables from different dimensions based on the massive operation data, in order to build the fault detection and diagnosis model. This approach exceeds other methods in terms of calculation speed and diagnostic accuracy, provided that the label data is reliable. However, this approach still faces significant challenges before it can be widely adopted. These challenges include uncertainties in the quality and quantity of training data, interpretability of models that are not supported by physical logic, portability of models across complex HVAC systems, and data privacy protection problems, etc. In contrast, the other two fault detection and diagnosis methods depend on the theoretical knowledge and the physical logic relations of an HVAC system, as well as the engineering experience of the testers. These two methods still perform well when there is insufficient information, and show good interpretability and universality of model. In summary, the development direction of the general fault detection and diagnosis method of HVAC systems may be the combination of the data-driven method and the knowledge-analytical method. This would enable the computer to possess the requisite knowledge of fault detection and diagnosis.

3.2 Application of Operational Fault Diagnosis in HVAC Systems at the Component or Subsystem Level This subsection will review the application of the above methods in the field of HVAC systems. With the combination of the conditional relationship in the model and operating characteristics of HVAC systems, many scholars have studied the fault detection and diagnosis of HVAC systems under the rule-based expert method. Schein and Bushby developed a rule-based, system-level fault detection and diagnostic method for HVAC systems [27]. For air handling units, a set of expert rules for fault detection

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and diagnosis was established [17], which was tested to be feasible through experiments in subsequent studies [28]. Some scholars have advocated the model-based approach. The gray-box model is a typical simplified physical model for tracking the steady-state response and detecting abrupt faults [35]. Many researchers have used gray-box models for fault detection and diagnosis in HVAC systems [29, 34]. In addition, the method based on black-box fitting model is also considered mainstream due to the advantage of the relatively easy training involved [40]. Recently, with the development of information technology in the building field, the application of artificial intelligence methods into HVAC systems for fault detection and diagnosis has become a mainstream trend. Li et al. [20] investigated the chiller operational problems with a two-stage data-driven approach based on the linear discriminant analysis. Cai et al. [5] developed a novel method for analyzing faults in ground-source heat pump systems. Cai’s model achieves multi-source information fusion-based fault diagnosis by applying Bayesian networks based on sensor data. Additionally, Han et al. [15] proposed an automated fault detection and diagnosis strategy for vapor-compression refrigeration systems, combining the principle component analysis feature extraction technology and the multiclass support vector machine classification algorithm.

4 Impacts of Operational Faults on Building Energy and Sustainability Goals As described above, the operation of the HVAC systems with faults may have a series of disadvantages. The causes and effects of a series of common HVAC operational faults were systematically identified in previous studies [3, 9]. These faults are ranked according to both the complexity of implementation and the severity of the associated energy penalty. Based on the ranking, four types of common faults were investigated in this section via native objects in EnergyPlus version 8.6 to illustrate the impact on building energy and sustainability goals. The specific details of the baseline model can be found in [38].

4.1 Impacts of Economizer Sensor Offset An air-side economizer is a type of mechanical device integrated into an air-handling system. It can introduce a specific amount of outside air as a means of cooling the indoor space when the outside air is cooler (in terms of dry-bulb temperature or enthalpy of the air) than the recirculated air. It can reduce cooling energy use and potentially improve indoor air quality by using more OA in locations with good OA quality. Many temperature and humidity sensors are implemented in the economizer to ensure its proper control and operations.

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An economizer outdoor air sensor is more likely to have an offset due to its exposure to outdoor conditions. The following two cases were modeled and simulated to estimate the impacts of economizer outdoor air sensor offset, using the native fault objects of Fault Model: Temperature Sensor Offset: Outdoor Air: ● Case 1: economizer outdoor air sensor with an offset of −2 °C. ● Case 2: economizer outdoor air sensor with an offset of −4 °C. The type of economizer in the case building is Differential Dry Bulb, meaning that the economizer will increase the outdoor air flow rate when there is a cooling load and the sensed outdoor air temperature is below the zone return air temperature. An outdoor air sensor offset fault can prevent the correct operation of the economizer. More specifically, a sensor with a negative offset reads a temperature that is lower than the true value, so the economizer opens the outdoor air damper even when the outdoor air temperature is higher than the return air temperature. The injection of hot outdoor air into the building by the faulty sensor and the open damper increases the cooling load for the coil and therefore increases the cooling energy of the building. A comparison of cooling energy consumption in several US cities is provided in Fig. 2. As can be seen in the figure, Case 1 leads to a cooling energy consumption increase of 0.8–5.8% compared to the fault-free case, while Case 2 increases the cooling energy consumption by 2.1–13.6%. Because the fault does not affect the supply air conditions as long as the cooling coil capacity is sufficient, indoor thermal comfort is usually not affected. The heating energy of the building is also not impacted.

Fig. 2 Impact of economizer outdoor air sensor offset on building cooling energy consumption

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4.2 Impacts of Thermostat/Humidistat Offset The thermostat/humidistat is a key control unit for HVAC systems. It is used to sense the temperature/humidity of air in a system or space so that the air condition is maintained near a desired level. Deviation from the thermostat/humidistat can result in faulty operation of the heating/cooling/humidifying/dehumidifying equipment. The following two cases are modeled and simulated to estimate the impacts of thermostat/humidistat offset faults, using the native fault objects of Fault Model: Thermostat Offset and Fault Model: Humidistat Offset: ● Case 1: humidistat offset caused by a dependent thermostat with an offset of 1 °C. ● Case 2: humidistat offset caused by a dependent thermostat with an offset of − 1 °C. In the case study, the humidistat offset fault is caused by the thermostat offset fault. These two types of faults have a coupling effect on the HVAC system control. Also, the humidistat offset is a function of the constant thermostat offset as well as the dynamic indoor air conditions. These make it challenging to estimate the fault impacts on system operations, either qualitatively or quantitatively. Comparisons of the energy consumption and occupant comfort in these case studies are depicted in Fig. 3 and Fig. 4, respectively. As can be observed in Fig. 3, both of the faulty cases lead to a remarkable influence on heating and cooling energy consumption in all of the investigated cities. Case 1 leads to a cooling and heating energy reduction of 12.47–28.19% compared to the fault-free case, while Case 2 increases the cooling and heating energy consumption by 19.07–34.24%. Figure 4 shows that the fault also dramatically changes the set-point unmet hours during heating and cooling periods, indicating significant impacts on occupant thermal comfort levels.

4.3 Impacts of Heating Coil Fouling As the most widely used type of heat exchanger heating and cooling coils are vulnerable to fouling faults. A reduced overall heat transfer coefficient causes reduced coil capacity, resulting in unmet loads and/or increased water flow rate and decreased water side temperature difference (“low ΔT” syndrome). In the case buildings, a main hot water heating coil and multiple hot water reheating coils are utilized, all of which are modeled via Coil: Heating: Water. Fouling can significantly impact the heat transfer performance of the coil, especially when the water quality is poor. The following two cases are modeled and simulated to estimate the impacts of fouling on heating coils in the building: ● Case 1: heating coil fouling leads to a 25% reduction of the coil’s overall UA (U-factor times area) value.

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Fig. 3 Impact of the integrated thermostat/humidistat offset faults on the building cooling and heating energy consumption

Fig. 4 Impact of the integrated thermostat/humidistat offset faults on the building’s cooling and heating energy consumption

● Case 2: heating coil fouling leads to a 50% reduction of the coil’s overall UA value. The fouling of the coil causes extra heat transfer resistance and decreases the coil capacities. This can lead to insufficient heating of the supply air, and then further affect indoor comfort levels. Figure 5 displays the impact of coil fouling in the case study. It shows that the fault leads to a dramatic increase in unmet heating hours for all the investigated cities, indicating significant impacts on occupants’ thermal

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Fig. 5 Impact of coil fouling on indoor thermal comfort

comfort. Note that the fault does not necessarily increase the heating energy of the building, because of the decrease of the heating coil capacity caused by the fouling.

4.4 Impacts of Dirty Air Filters Air filters are implemented at the OA inlets or other duct locations to reduce the amount of particulate matter or contaminants that are brought into the air system. Dust, debris, or other blockages can gradually accumulate on the air filter during its operations. Increased air loop system resistance results in a different system curve. This directly affects the operation of corresponding fans. More specifically, it may lead to an increase in the fan pressure, fan energy consumption, and/or the enthalpy of the fan outlet air. It may also lead to a reduction in the airflow rate and thus affects the performance of other system components (e.g., heat transfer performance of heating/cooling coils). The following two cases are modeled and simulated to estimate the impacts of dirty air filters in the case building, using the native fault objects of Fault Model: Fouling: Air Filter. Note that a fan curve also needs to be provided in the fault modeling to calculate the airflow rate decrease due to the increase of fan pressure drop, and the curve needs to cover the design point specified in the fan modeling. ● Case 1: air filter fouling leads to a 10% increase in the fan pressure at the rated condition. ● Case 2: air filter fouling leads to a 20% increase in the fan pressure at the rated condition. The case study implements a variable speed fan for the air system, on which the fault applies. In both situations, the fan flow fraction at the faulty case is higher than

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Fig. 6 Impact of an air filter fouling on fan energy consumption

the fault-free case which leads to increased fan energy consumption. Figure 6 plots the comparison of fan energy consumption in the case buildings across the investigated cities. As can be found in the figure, both faulty cases lead to a significant increase in the fan energy consumption. Case 1 leads to a fan energy increase of 4.32–5.30% compared to the fault-free case, while Case 2 increases the fan energy consumption by 7.41–9.52%. Since the variable speed fan can usually maintain the airflow rate by shifting to a higher speed, heating, and cooling energy consumption, as well as indoor thermal comfort, are not significantly affected in the case study.

5 Conclusions Operational faults are commonly found in building HVAC systems, posing significant challenges to achieving optimal energy efficiency and occupant comfort in smart and sustainable buildings. These faults can lead to deviations from design expectations and result in unnecessary energy consumption, increased operating costs, and potential equipment damage. To address these issues, fault modeling and diagnostic analysis are essential tools for detecting, analyzing, and rectifying operational faults in the context of smart and sustainable buildings. In this chapter, we have delved into various representative operational faults in buildings, exploring their underlying causes and potential risks. By understanding the root of these faults, we can take proactive measures to prevent their occurrence and mitigate their impact on the smart and sustainable built environment. We have also examined the intricacies of modeling and analyzing operational faults, considering the complex interrelation and coupling effects that exist within building energy systems in the pursuit of smart and sustainable building operations.

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In our analysis, we have compared different fault detection and diagnosis methods, highlighting their respective strengths and limitations in the context of smart and sustainable buildings. These methods encompass rule-based expert systems, modelbased approaches, and data-driven techniques. While each approach has its advantages, the future direction lies in combining data-driven insights with knowledgebased analytical approaches for smart and sustainable building management. This fusion will enable a deeper understanding of the faults, leading to more accurate fault detection and diagnosis, supporting the energy efficiency and sustainability objectives of smart buildings. As we look ahead, the field of fault modeling and simulation tools is poised for advancements that will revolutionize building energy efficiency and sustainability efforts. By harnessing the power of emerging technologies and big data in the context of smart and sustainable buildings, we can develop more sophisticated fault detection systems, allowing for real-time monitoring and swift fault correction. Proactive fault management will lead to improved occupant comfort, reduced energy waste, and a more sustainable built environment in the realm of smart buildings. In conclusion, addressing operational faults in building HVAC systems is vital for achieving energy efficiency and occupant comfort. By employing fault modeling and diagnostic analysis, we can pave the way towards smarter, more resilient buildings that optimize energy consumption while prioritizing occupant well-being and sustainable practices. Embracing these advancements will undoubtedly play a crucial role in shaping the future of building systems, fostering a greener and more sustainable world for generations to come.

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Innovative Technologies for Enhancing Sustainability in Buildings and Cities

Urban Heat Adaptation and a Smart Decision Support Framework Bao-Jie He, Ke Xiong, and Xin Dong

Abstract Many cities are significantly threatened by urban heat challenges, which are the combined result of heatwaves and urban heat island (UHI). Nevertheless, urban heat will be further aggravated due to upward trends of global warming and urbanization. Adopting mitigation and adaptation strategies is of vital importance to secure societies against urban heat threats and vulnerabilities. Whilst many mitigation techniques have been explored, there is a lack of real-time and intelligent guidance to accurately inform people about heat-related impacts and adaptation strategies. Therefore, this study aims to frame the development of a smart urban heat adaptation decision-making system while considering impact assessments and the selection of an adaptation strategy, particularly at the community level, which concerns the high heterogeneities of local climates and people’s activities. This chapter presents the definition and goals of urban heat adaptation, followed by the measures and assessment indicators. Afterwards, existing decision support tools relevant to urban heat mitigation and adaptation are reviewed. Then, an urban heat adaptation system, in connection with a smart decision-making concept, is framed in terms of original heat data collection, heat impact prediction and visualization, and adaptation strategy selection. Finally, a demonstration of a smart path selection for urban heat adaptation is presented. Overall, this study provides support for the accurate assessment of urban heat in communities and contributes to the smart development of sustainable and resilient communities.

B.-J. He (B) · K. Xiong · X. Dong Centre for Climate-Resilient and Low-Carbon Cities, School of Architecture and Urban Planning, Chongqing University, Chongqing, China e-mail: [email protected] B.-J. He Network for Education and Research On Peace and Sustainability (NERPS), Hiroshima University, Hiroshima, Japan Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education, Chongqing University, Chongqing, China State Key Laboratory of Subtropical Building Science, South China University of Technology, Guangzhou, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 T. Zhou et al. (eds.), Smart Buildings and Technologies for Sustainable Cities in China, Urban Sustainability, https://doi.org/10.1007/978-981-99-6391-1_6

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Keywords Extreme heat · Urban heat adaptation · Smart concept · Outdoor thermal comfort

1 Introduction Urban heat, which arises from the combined effect of heatwaves and urban heat islands (UHIs), is a common problem for many cities, especially those in tropical and subtropical climates [14, 18]. Extreme heat deteriorates thermal comfort and can possibly lead to an increase in mortality and morbidity by exerting heat stresses over human bodies [25, 32]. Extreme heat is one of the most challenging climate-related hazards, with a high fatality rate in many countries such as Australia, the United States, the United Kingdom, India, and China [4, 6, 17]. The most notorious case took place in Europe in 2003, killing about 70,000 people across 12 countries [2]. After about 20 years, however, these societies are still not well prepared for addressing heat challenges. The most recent case in England indicates that extreme heat is still outstanding, causing more than 2500 excess deaths in 2020 [26]. Despite this, heat challenges are more prominent in developing countries. By comparing heat-induced mortality across different countries, it is found that an average mortality of 32.1 per million people occurred in China from 1986 to 2005 [34], while it was 0.75–2.20 per million people in the US between 1999 and 2014 [9]. In addition, extreme heat is also one of the key drivers for various diseases related to the respiratory system, digestive system, cardiovascular system, skin heat damage, eye, and metabolic and urinary systems [16]. Apart from mortality and morbidity, extreme heat also hinders urban sustainability in many aspects. To alleviate heat-induced thermal discomfort and heat stresses, the reliance on air-conditioning facilities leads to a significant increase in electricity use [30]. Each degree of temperature increase could lead to an 8.5% increase in electric load, based on base temperatures of 17 and 20 °C in Louisiana and Maryland, respectively [28]. Therefore, electricity use may double when the temperature increases to about 29 and 32 °C, which are common under the impacts of both heatwaves and UHIs. Meanwhile, temperature increase in cities also leads to water use increase for irrigation and water-related cooling strategies and behaviors [13]. Moreover, extreme heats aggravate social inequities. For instance, the availability and operability of air-conditioning and cooling facilities foster economic inequalities. Heat-induced mortality and morbidity are more prominent among vulnerable groups such as children, the elderly, pregnant women, and patients, compared with others [19]. Men can be more vulnerable to physiological impacts and the disruption of their daily functioning, as male groups are not prepared for and experience a higher likelihood of heat exposure [16]. Extreme heat further causes economic losses because of the reduction in labor productivity and additional impacts on agricultural yield, health, transport, energy, and assets [21]. It is projected that annual economic losses, with only consideration of labor productivity, will double to about US$200 billion in 2030 and US$500 billion in 2050. In China, according to the RCP8.5 scenario, the

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reduction in heat-induced labor productivity will lead to an annual economic loss of US$1.0–1.5 trillion by 2050 [35]. Addressing heat challenges not only requires carbon neutrality for greenhouse gas reduction, but also needs a series of urgent strategies for alleviating heat-induced impacts by building heat-resilient cities and communities. In particular, heat-resilient cities and communities are expected to anticipate, prepare for, and adapt to extreme heat by planning, designing, building, and operating. This ensures the society can withstands, responds to, and recovers rapidly from heat-induced disruptions. This requires a collection of actions including (i) monitoring, forecasting, and warning, (ii) urban heat mitigation and adaptation, (iii) implementation of a policy and regulatory landscape, and (iv) the development of a co-benefits approach (Fig. 1) [14, 17]. Monitoring, forecasting, and warning systems can be adopted to observe and collect heat-related information, convert them into health information, and thereby inform citizens of heat risks and vulnerabilities. This information can directly improve people’s awareness and knowledge of dealing with heat challenges. Accordingly, citizens can potentially take either mitigation or adaptation strategies to reduce heatrelated impacts or avoid heat exposure, and different stakeholders of the society can provide essential services (e.g., cooling shelters, facilities, health care). Policy and regulatory landscapes are also important to promote the society to strengthen existing environments for urban heat mitigation and adaptation. The co-benefits approach is a cost-effective strategy to include the mitigation and adaptation strategies through implementing other sustainable practices (e.g., green building, water sensitive urban design, forest cities, green roofs) [14]. Urban heat mitigation and adaptation are particularly important factors in creating heat-resilient cities and communities. The implementation of mitigation strategies reduces temperature, improves thermal comfort, and alleviates many other negative impacts. A sound heat mitigation system for urban planners and designers has been developed through extensive studies on water-related cooling strategies, green infrastructure, innovative reflective and permeable materials, and urban design for ventilation enhancement and solar radiation prevention [17]. The implementation of adaptation strategies, consisting of governmental, public, and individual strategies, can protect people from heat-related hazards. Nevertheless, to effectively address urban heat challenges, there is a need to optimize the adoption of mitigation and adaptation strategies by a chain procedure of identifying climatic and built contexts, determining performance metrics, selecting applicable strategies, determining key design variables, and optimizing strategies [27]. A decision support system is effective in supporting such a chain process. There have already been some decision support systems developed for urban heat mitigation. However, there is little understanding of the use of decision support systems on urban heat adaptation, within the constraints of the societal capacity of avoiding heat risks and reducing vulnerabilities. Therefore, this chapter aims to introduce the preliminary outcomes on a decision support system for urban heat adaptation by conducting impact assessments and determining adaptation strategies. This decision support system is expected to be developed into an advanced platform, namely a smart urban heat adaptation decisionmaking system. The decision support system presented here is particularly developed

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Fig. 1 Actions towards heat resilient cities and societies

at the community scale to support people’s daily community activities. Meanwhile, the community scale takes into account the high heterogeneities of local climates. Existing studies have indicated that people only have a limited heat awareness and knowledge, and do not have enough knowledge on solutions. Therefore, a decision support system on urban heat adaptation is an important tool to overcome such a societal challenge by offering citizens real-time instructions and suggestions for immediate actions. The remaining contents are structured into four sections. Section 2 first presents the definition and goals of urban heat adaptation, after which the three kinds of heat adaptation are described and the measurement and indicators of urban heat adaptation are defined. Following this, existing decision support systems relevant to urban planning and design are reviewed, and the significance of developing a decision support system for urban heat adaptation is presented (Sect. 3). Afterwards, Sect. 4 frames the urban heat adaptation system, with the consideration of the smart decisionmaking concept, in aspects of original heat data collection, heat impact prediction and visualization, and adaptation strategy selection. In particular, this section presents an ideal demonstration of path selection under extreme heat. Finally, Sect. 5 concludes this chapter.

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2 Urban Heat Adaptation System 2.1 Definition and Goals Within climate change, adaptation specifically refers to the reduction of the negative consequences of the changing climate through adjustments in both policy and actions to promote greater resilience to current and future impacts. ‘Urban heat adaptation’ refers to adjustments of policy and actions to reduce the hazards of urban heat. Heat adaptation does not necessarily reduce urban air and surface temperatures, heat stress, or outdoor thermal comfort, but it protects people from heat-induced impacts. For instance, air-conditioning in buildings is thought of as the most effective adaptation strategy to relieve people of high indoor temperatures, especially for vulnerable groups, and to ensure their health and safety. Nevertheless, airconditioning systems consume electricity, resulting in an increase in urban energy use. Furthermore, waste heat released from the indoors to the outdoors may aggravate urban heat by elevating outdoor temperatures and worsening heat island effects. As a result, the indoor–outdoor heat transfer locks indoor–outdoor temperature increases into a loop. It is estimated that by the end of the twenty-first century, the average temperature increase in US cities will reach 1–2 °C, at which point the energy demand will have risen by 5–25% [11]. Empirical studies in Japan have indicated that the operation of air-conditioning during the heatwaves in 2013 and 2018 resulted in an increase in urban temperatures by 0.07 and 0.11 °C, respectively [31]. Nevertheless, global and local temperature increases foster the use of air-conditioning systems, and by 2050 it is estimated that each person will own and use an air-conditioner, meaning that the impact of air-conditioning related waste heat will be aggravated [14]. Such an example indicates that adaptation strategies: ● Aim to provide people with a comfortable, safe, healthy, and livable environment which lowers heat-related health impacts; ● May generate associated negative impacts on the environment, society, or economy, and sometimes the negative impacts will be fostered in the long run; ● Underperform in cost effectiveness compared to mitigation strategies which counterbalance urban heat challenges, but generate immediate cooling effects.

2.2 Adaptation Methods and Measures To improve the societal capacity of adapting to heat and protecting individuals, various efforts are required to improve individuals’ awareness and knowledge, enhance individuals’ actions, and improve heat-resilient infrastructure.

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Body Thermal Resistance

Improving people’s awareness and scientific knowledge of heat adaptation is a critical issue, as various empirical studies have reported on individuals’ low awareness of extreme heat and its associated impacts [17]. Furthermore, the knowledge of how to cope with extreme heat is even lower [15]. This means that individuals are relying on the body’s thermal balance and the ability of the body to endure heat to overcome extreme heat without fully understanding the impact of extreme heat on the body. However, under most situations, human bodies find it difficult to resist heat-induced impacts. Moreover, this heat resistance is extremely delicate, and heatinduced diseases and even death may occur if the body fails to work. Therefore, to improve individuals’ adaptability to extreme heat, it is recommended to properly regulate individual thermal adaptation according to heat characteristics. Overall, the improvement of heat-related awareness and knowledge is both an important start and premise of heat preparation.

2.2.2

Individual Thermal Adaptation

A proper and scientific improvement of an individual’s thermal adaptation, according to the Center for Disease Control and Prevention, depends on enabling three criteria, namely: staying cool, hydrated, and informed. Staying cool means that people need to wear appropriate clothing (e.g., lightweight, light-colored, loose-fitting clothing), take advantage of cool indoor areas (i.e., stay in an air-conditioned place as much as possible), schedule outdoor activities carefully, pace themselves (i.e., cut down on exercise during the heat), wear sunscreen, not leave children in cars, and avoid hot and heavy meals. Staying hydrated suggests that individuals should drink plenty of fluids (i.e., drink more fluids, regardless of how active they are), stay away from very sugary or alcoholic drinks (as these result in higher levels of body fluid loss), and replace salt and minerals. Staying informed means checking for updates (i.e., checking the local news for extreme heat alerts and safety tips), recognizing the signs (e.g., heat risk signs, heat adaptation signs), and monitoring those at high risk [5]. Changing behaviors to enhance thermal adaptability is simple and flexible, allowing individuals to make their own adjustments. The adjustments are achievable in people’s daily functions. First, individuals can adapt through adding or removing clothing, holding an umbrella, and wearing a hat. Second, individuals can change their diet pattern to reduce heat stress, while they should also avoid taking much heavy food and eat some cold meals appropriately. Under extreme heat conditions, food may become spoiled or contaminated, which should be highly monitored. Third, people may reschedule activities and work, shorten their work durations, and lower their work intensity to avoid and reduce peak heat stresses. Fourth, it is desirable to improve indoor cooling capacities through fans and air-conditioning systems, to transfer outdoor venues to the indoors, and transfer exposed areas to shaded areas. Fifth, increasing the reliance on private cars, trams, and trains that are equipped with air-conditioning systems instead of walking and cycling is also recommended.

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However, such measures are limited to individual behaviors, and individuals’ demographic characteristics are different, which can result in a diverse range of heatinduced measures. Therefore, adaptation on an individual scale needs to consider population-sensitive heat impacts, since improper adaptation could possibly cause further damage. Overall, the most meaningful urban heat adaptation should be that of public communication, which would provide individuals with accurate warning information and proper adaptation actions.

2.2.3

Public Thermal Adaptation

The aforementioned suggestions are primarily catered towards individuals; their normal living, activity, and working patterns are mostly compromised to indoor and in-vehicle spaces. An active adaptation system should therefore ensure individuals’ normal living, activity, and working status, especially in outdoor and semi-outdoor spaces. A public adaptation system, which is also the most comprehensive adaptation system, is therefore expected to involve collective efforts from various disciplines and support from various levels of government. Generally, public thermal adaptation may be considered to take orderly and conscious thermal adaptation measures, rather than disordered and unconscious self-heating adaptive behaviors, in order to reduce extreme heat impacts and thermal hazards. Achieving public thermal adaptation requires high availability and operability of adaptation measures. Adaptation measures are defined as interventions which can protect people from heat. On the one hand, interventions can be soft infrastructure, such as a national heat response plan, heat health information, community network and communications, enterprise and societal operation, and personal behavioral changes. On the other hand, interventions can be techniques supporting urban heat information collection and prediction, information and knowledge transfer, and cooling centers and associated facilities. Actions of public thermal adaptation can be taken at national or state government, local government, community, and industry scales [8]. Among national and state governments, a comprehensive heat response plan or heat health plan should provide extreme heat information (e.g., impact and severity, comfort and health pressure), the heat-resistance responsibility of different stakeholders (e.g., governmental departments, local government, industry, and individuals), heat preparation and actions, and associated effectiveness. Until now, various nations such as the United Kingdom, United States, Australia, India, Saudi Arabia, Germany, and New Zealand have released national/state heat response plans [14]. Local governments play an important role in putting national/state recommendations into practice, such as the development of early warning systems and the establishment of urban cooling centers, measures to improve awareness of risk factors, heat-related illness symptoms and heat signs, and the education about when and how to seek treatment [20]. Moreover, Local governments, in turn, have the responsibility to develop local heat resistance guides for the communities and enterprises through identifying local heat impacts. Communities have the responsibility of checking on family, friends, and neighbors to ensure their access to air-conditioning, proper

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functioning of energy and water systems, and heat warning information. Operational pattern changes for heat adaptation among different industries play a fundamental role in providing adaptation measures to individuals and communities. For instance, energy and water service providers should upgrade energy and water infrastructure to meet the increasing and peak demands for energy and water. Healthcare and medical facilities (e.g., ambulances, emergency responders, hospital wards) should work properly and medical staff should be prepared for heat emergencies. Heat insurance should be available for individuals or enterprises to support their recovery after heat attacks. Urban planners and designers should increase the consideration of heat health through properly adding cooling centers. It should be noted that enterprises should follow the standards, laws, and regulations on heat resistance to protect the workers’ thermal comfort and heat health. In some cases, enterprises should even change the working hours to avoid hazardous heat stresses [14].

2.3 Measurements and Indicators The overall goal of heat adaptation is to ensure individuals’ comfort, health, and safety, on which basis heat adaptation for enterprises and societies aims to enhance work productivity, improve social equality, and avoid economic losses. Such an expectation requires the usability, walkability, bikeability, entertainability, and workability of public spaces (e.g., outdoors, semi-outdoors, indoors) as much as possible. Such measurements can be assessed by temperature, heat stresses, and thermal comfort, where usability, walkability, bikeability, entertainability, and workability are compromised when the indicators exceed the limits. Air temperature is an effective assessment indicator of extreme heat, and the air temperature can be directly obtained by meteorological data. It is essential to reduce outdoor activities after noon and to take heat-prevention measures for (diurnal) working when the air temperature is higher than 35 °C over three consecutive days. Avoiding working or other outdoor activities at high temperatures (i.e., during a day when the temperature reaches 37 °C) is suggested [29]. According to the definition of a heatwave, temperature limits can be fixed or fluctuating with specific percentiles. Alternatively, many studies have investigated the critical temperatures of heat-induced mortality and morbidity, beyond which temperature, heat-induced illnesses, and deaths can increase to a large extent [36]. The WBGT, which considers the influence of air temperature and air humidity, is a factor assessing heat stresses. The WBGT was initially used for determining the heat stress among industrial hygienists, athletes, sporting events, and the military [1], with the integration of solar exposure, air temperature, humidity, and wind speed. Figure 2 presents the suggestions of activities according to the WBGT within different climates. For instance, when the WBGT exceeds 32.3 °C in hot climates, exercise should be cancelled; when the WBGT ranges between 25.7 and 27.8 °C in hot climates, activity can be conducted normally but it is necessary to monitor fluids. Since individuals who are in hot climates can gain stronger heat resistance,

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Fig. 2 Heat safety guidelines for low-risk acclimatized individuals in different climate zones based on ACSM guidelines [12]

the WBGT thresholds can be higher than the ones in mild and cold climates. Furthermore, previous studies have also linked the WBGT with heat-induced morbidity and mortality [33]. Numerous assessment indicators of outdoor thermal comfort have been developed, among which the physiological equivalent temperature (PET) and Universal Thermal Climate Index (UTCI) are the two most popular. Existing studies have determined the limits of the different levels of outdoor thermal comfort (Fig. 3). Furthermore, outdoor thermal comfort has been converted into heat stresses. For instance, a UTCI value above 46 °C means extreme heat stress, and a UTCI value ranging between 38 and 46 °C means very strong heat stress. In comparison, the PET limits are different; a PET value above 41 °C means extreme heat stress, and a PET value ranging between 35 and 41 °C means strong heat stress. Due to the different climates of Western/Middle-European countries and subtropical regions and the long-term heat adaptation of individuals, the PET limits also exhibit some differences.

3 Decision Support Tool for Smart Urban Heat Adaptation 3.1 Definition and Benefits Whilst a sound heat adaptation system has been proposed, there are various challenges and barriers for practically improving societal heat capacity. For instance, a public heat adaptation system is relevant to various stakeholders with different responsibilities. Moreover, there is still a large gap in the effective transfer of heat health information and scientific adaptation measures to individuals. Therefore, the development of a decision support system which refers to a computer-based tool for decision analyses and participatory processes for heat adaptation is needed to address such gaps. A decision support tool for smart urban heat adaptation is expected to enable different kinds of stakeholders to turn heat adaptation plans, guides, and technologies

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Fig. 3 Associations of heat stresses with physiological equivalent temperature (PET) and Universal Thermal Climate Index (UTCI) [3, 22, 23]

into operational and behavioral changes to communities, industries, and individuals. For instance, through the decision support tool, individuals with different demographic properties (e.g., age, health, gender, economic conditions) can be informed of accurate information relevant to activity types, locations and paths, time and duration, and the availability of relief facilities and cooling centers. The tool can also monitor heat spots and estimate the peak times and hot spots of energy demands so that the energy supply and redistribution can be reliable and blackout events can be avoided. For urban planning and design, the tool can further identify the most vulnerable areas with a consideration of the economic, health, and age situation of communities; furthermore, the locations of cooling centers, healthcare centers, and water fountains can be determined. The tool can also support the performance assessment after configuring heat adaptation facilities and techniques. Furthermore, the decision support tool can alleviate heat-induced impacts and reduce social injustice. For instance, the tool can reduce both human and environmental vulnerability by improving heat resistance among vulnerable groups. An increase in heat adaptation capacity among enterprises and companies means a concurrent increase in work productivity and economic production.

3.2 Examples of Decision Support Tools for Heat Challenges There have not yet been any decision support tools developed for heat adaptation. Nevertheless, decision support tools can transfer guides into practice. Around the globe, various climate-related decision support tools have been developed, which can generate some implications for heat adaptation. Such existing models are primarily

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produced by government agencies, departments, and academic and research institutions [10]. The decision support tools that have been developed are mainly in the following fields: ● ● ● ● ● ● ● ● ● ●

Planning new transportation routes; Relocating core infrastructure; Improving emergency response systems; Planting trees; Creating composting services; Improving energy efficiency; Developing clean energy sources; Creating new parks and greenways; Greening roofs and alleys; Urban heat island mitigation and microclimate regulation [10].

In particular, there have been some tools developed for urban heat island mitigation. For instance, the microclimate and urban heat island mitigation decision support tool (UHI-DS) was developed by the University of New South Wales, Australia [7]. It aims to enable governments and built environment industries to inform urban policy, development assessment, and planning practices related to potential building and urban cooling interventions. Decision support tools can be web-based, software, or custom-based support tools for cities or regions. UHI-DS is a custom-based tool that can integrate scientific models and a variety of cooling techniques within the context of different precincts and climates. In particular, it assesses urban heat challenges in aspects of air temperature and outdoor thermal comfort, tailoring to the precinct context, and it provides urban planners and designers with several suggestions on how to address the urban heat island problem through the use of reflective materials, urban greenery, green roofs, shading, evaporative cooling facilities, water bodies, and the built form. Overall, UHI-DS is a convenient, easy, and efficient tool for governments, developers, and planners to address the vulnerability to urban heat island effects. However, custom-based tools are generally expensive and time-consuming to build due to their precinct-based features. Therefore, the tool has only presented the case studies of Green Square Town Centre, Macarthur Heights, and Paramatta Civic Link, Australia so far [7]. A comprehensive decision-making framework of urban heat mitigation techniques has further been developed to support local governments, urban planners, and designers to select proper mitigation techniques. Compared to UHI-DS, the framework has not only considered the cooling performance of different techniques, but it has also included environmental, social, and economic metrics (e.g., electricity consumption, outdoor thermal comfort, morbidity and mortality, economic productivity) [27]. A parallel function of the framework is to enable designers to identify suitable cooling techniques for different sites (e.g., pavement, street, façade, roof) and different climates. Afterwards, the tool will prioritize more effective strategies through their comparisons with the base model, based on which it can further generate combinations of different strategies over different sites (e.g., pavement, street, façade, roof) which can generate stronger cooling effects. This method employs all possible

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areas for cooling. On this basis, a genetic algorithm is employed to optimize the cooling effects of all possible strategies through quantitative analysis. During this, different cooling variables (e.g., tree species, configuration, tree height) are adjusted in value or typology to generate the optimal cooling recommendations. The effectiveness of the tool has been tested within the context of Leppington, Australia [27]. Beyond the decision support tools for heat mitigation through the urban design process, a tool which can support urban heat island mitigation through planning regimes has also been developed. For instance, [24] developed an easy-to-use decision support tool to enable urban planners to understand how different parameters affect street-level heat island effects. Using a tree-regression method, the tool identified possible relationships between urban morphology, socio-economic features, and urban heat island effects. Afterwards, the tool categorized the street types according to their temperature levels. Therefore, the user could retrieve the heat island assessment matrix to understand how factors influenced the urban heat island. The tool was applied to the city of Montreal, Canada with the consideration of specific morphological features (e.g., average building height, building standard deviation, building height to street width ratio, built-up density, and vegetation density) and socio-economic characteristics (e.g., façade materials, dominant land-use, population density, and average traffic count). Finally, the heat island assessment matrix exhibited five levels of urban heat islands (e.g., low, medium–low, medium, medium– high, and high) with different parameter thresholds. For instance, the low UHI cluster had low building density, low building height, abundant parks and conservation areas, high vegetation density, low traffic, and low population density. In comparison, the high UHI cluster had the highest building density (32–51%), the highest building height, commercial and industrial land use, and concrete and glass-dominant façade materials [24]. Overall, existing studies have primarily conducted mitigation strategies at the precinct or neighborhood level. The development of decision support tools for urban heat mitigation has generated a general pattern of addressing heat impacts, including: ● Identification and collection of climatic conditions, urban morphological characteristics, and socio-economic features; ● Determination of the metrics of urban heat and its associated impacts (e.g., environmental, social, economic, and health aspects); ● Assessment and estimation of urban heat based on scientific models and urban morphological characteristics, and the conversion of urban heat into heat impacts; ● Selection of cooling techniques and strategies, changes in cooling variables, and prediction and optimization of cooling performance; ● Recommendations of the proper cooling techniques and strategies, and evidencebased urban heat mitigation decisions in urban planning and design.

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4 Framework of a Smart Decision Support Tool for Heat Adaptation 4.1 Local Heat Adaptation Following the decision support tools for urban heat mitigation, a smart decision support tool for heat adaptation would concentrate on public adaptation strategies at the precinct or neighborhood scale as well. In particular, the precinct or neighborhood consideration can: ● Address the actual gap of high-resolution identification of heat-induced impacts and accurate solutions. This is because existing weather monitoring systems are prepared for district-level heat impacts, while the street- or block-level heat impacts cannot be detected accurately. ● Exhibit local features of temperatures. Urban climates are locally heterogenous, associated with the differences in surface cover and surface structure, where the local climate zone classification scheme has widely exhibited the local temperature variations. ● Represent community-level heat vulnerabilities. Socio-economic characteristics are mostly homogeneous within a community in terms of economic level, ethnics, education level, age, and their awareness and knowledge of dealing with urban heat challenges. ● Meet residents’ daily functional requirements. The local residential and commercial neighborhoods are key living and working areas; with increasing interest being paid to the 15-min city concept for proximity, this highlights the significance of local heat adaptation.

4.2 Structure of Smart Decision Support Tools Figure 4 presents the framework of a smart decision support tool for heat adaptation. Overall, it consists of six components: data collection, climate prediction, data postprocessing, heat health information storage, information retrieving, and decision or recommendation generation. First of all, it is important to note that the information across the whole process is developed at the community level, while the suitability and feasibility at other scales depend on the demands of the required resolution. Data collection is used to acquire macro and microclimate data to fortify weather-related models. Macroclimates are regional climates and the associated seasonal and day– night changes. In comparison, microclimates are the microclimatic parameters and morphological characteristics (Fig. 5). Weather-related models include the Weather Research and Forecasting (WRF) Model, the Parallelized Large-eddy Simulation Model (PALM), ENVI-met, and other CFD models. The WRF model can be used to estimate large-scale weather events with low resolution, while ENVI-met can predict the climates on the building, street, block,

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Fig. 4 A framework of the smart decision support tool for urban heat adaptation

Fig. 5 Environmental, social, and economic data and the associated sources

and precinct scale with high resolution. The environmental parameters include air temperature, surface temperature, wind speed, relative humidity, and globe temperature. Such parameters are expected to be processed as indicators that are closely related to heat comfort and health (e.g., mean radiant temperature, PET, UTCI). Thermal comfort and health information are pre-stored into the platform, which users can then retrieve for possible adaptation strategies. On the one hand, the users can be residents with various demographic characteristics (e.g., age, health) and workload intensity. On the other hand, the users can be service suppliers and community healthcare and cooling centers. Moreover, the adaptation system is expected to send warning alerts to the users if they are well-linked through the communication

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channels. Afterwards, more detailed suggestions on how to adapt to extreme heat are expected to be provided. The tool is expected to operate a “platform-individual” mode by estimating real-time heat challenges, calculating heat stimuli based on activity and demographic characteristics, comparing body resilience capacity and heat stimuli, generating warning information and suggestions, and sending such information to particular residents. One precondition to achieve this is to get permissions for different residents’ demographic, activity, and location information. If not, users should retrieve warning information and suggestions through their own access to the platform. Whilst the current chapter showcases the significance of avoiding urban heat challenges through adaptation strategies, mitigation strategies and techniques (e.g., green and blue infrastructure, innovative materials, urban and building form modification) are important to alleviate urban heat challenges, especially in areas where the strong heat challenges are difficult to adapt to. Therefore, the integration of mitigation strategies and techniques into communities is critical for the development of heat-resilient cities and communities. Figure 5 presents the data (and its associated sources) required by a smart decision support tool in environmental, social, and economic aspects. The urban form/ morphology data consists of land use land cover, building footprints, urban canyon information, permeable and impermeable surfaces, surface materials, and vegetation cover. The data can be obtained via planning departments, or collected through remote sensing products (e.g., MODIS, Landsat, Lidar, Drone) and relevant firms and individuals. The microclimatic data can be surface temperature, air temperature, relative humidity, wind speed, solar radiation, mean radiant temperature, and heat-related pollutants (e.g., NOx , O3 ). The microclimatic data can be collected through remote sensing, national weather stations, city weather stations, microclimate stations, and numerical simulations. The socio-economic data can be reflected by demographic characteristics such as education, age, gender, income, social isolation, job types, health, and housing, which can be obtained through census data, questionnaires, and interviews.

4.3 Demonstration of Smart Adaptation Figure 6 presents an ideal and simple demonstration of the smart decision support tool for urban heat adaptation, with the aim of identifying the most suitable path from point A to point B. There are six possible paths in total, each with different urban morphological characteristics in terms of pavement materials, vegetation, water bodies, and shading features. During the path selection, an outdoor thermal comfort indicator (e.g., PET) can be adopted to measure the heat stress and determine the path suitability. For example, the ENVI-met tool was adopted to simulate the environmental variables that were needed to calculate the outdoor thermal comfort. Due to differences in the morphological characteristics, the outdoor thermal comfort conditions of the six paths were also diverse, as shown in Fig. 7 Here,

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Fig. 6 An ideal neighbourhood with spatially heterogeneous morphological characteristics

we specifically assessed the maximum heat stresses at 14:00. The results indicate that the heat stress of path 1 was the lowest, so path 1 was selected as the most suitable path for walking (Fig. 8). Nevertheless, it should be noted that the selection criteria could be different; when looking at the cumulative outdoor thermal comfort level, it exceeded the neutral value when considering the walking length. The criteria can also set an absolute threshold that once the calculated PET is exceeded, the path should be excluded. This case has merely demonstrated a simple smart adaptation system. In actual scenarios, smart decisions on how to adapt to extreme heat should be tailored to different kinds of groups. On the one hand, the demographic characteristics (e.g., gender, age, health condition) represent different levels of body resistance to heat stress, which means a difference in their adaptation threshold. On the other hand, different activities people perform in outdoor spaces imply thermal comfort heterogeneity, also suggesting a difference in heat stimuli. Such a group-specific requirement increases the complexity in the decision process, and thereby the workload increase in prediction, post-processing, and storage. To overcome this challenge, it is essential to improve the calculating speed for accurate and prompt decisions in practical applications. Determining the adaptation thresholds among different groups is also a critical task.

5 Conclusions Urban heat challenges are becoming increasingly more severe under climate change and urbanization, whilst the actions taken towards improving the heat-resilience of cities and societies are quite slow. Existing studies have mainly been focused on the development of cooling techniques and strategies. However, such cooling strategies have not been well implemented, even while temperatures have continued to increase. Therefore, this study suggests the significance of heat adaptation strategies

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Fig. 7 Simulated outdoor thermal comfort (PET) at 14:00 (top) and 12:00 (down)

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Fig. 8 The priorities of the paths according to the maximum outdoor thermal comfort level

and the urgency of developing decision support tools for heat adaptation. In particular, this chapter preliminarily discusses various heat adaptation goals, measures, measurements, and indicators, after which the importance of developing decision support tools was discussed. More importantly, this chapter presents how to develop a smart decision support tool for heat adaptation. Overall, this chapter provides a reference for promoting urban heat adaptation development and opens a window into the development of decision support tools for urban heat adaptation. Acknowledgements Research on the Establishment and Application of Green and Low Carbon Habitat Environment in Chongqing East Railway Station Area (Grant No. 2022-KJ-JY-20, CSTB2022TIAD-KPX0195). State Key Laboratory of Subtropical Building Science, South China University of Technology (Grant No. 2022ZA01). Guangdong Basic and Applied Basic Research Foundation (No. 2023A1515011137).

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Smart Heating in Centralized Urban Heating Systems Xiaojie Lin, Jiaying Chen, Wei Huang, and Wei Zhong

Abstract One cornerstone of China’s “Carbon Neutrality” roadmap is to realize the sustainable development of district heating systems to reduce their impact on the environment. As a key part of the district heating system in China, the urban centralized heating system (UCHS) has a wide range of energy sources. The clean, smart, and efficient operation of UCHS plays a critical role in such sustainable development. However, UCHS in China is still widely using technologies developed in the 1990s. To achieve a target clean heating rate of 70% by the year 2021, a new paradigm of system analysis, renovation, and operation has been developed. This chapter will focus on the recent work in clean heating technology, data-driven modeling, operation decoupling optimization, substation data mining, and the transition from UCHS to an integrated energy system (IES). The framework of the cyber-physical system (CPS) in China’s UCHS systems is defined. The effectiveness of such a methodology has been demonstrated in both UCHS and future IES cases. Keyword Mart heating · Cyber-physical system · Smart energy

1 Urban Centralized Heating System and Its Challenges In China, the urban resident population reached 914 million by the end of 2021, accounting for 64.72% of the total population [15]. An urban centralized heating system (UCHS) is the key to a “Smart City” as it concerns the quality of the residents’ livelihood. Hot water was used as the working medium of UCHS in China. As shown in Fig. 1, a typical UCHS consisted of a series of distributed heating sources. It mainly uses hot water as a working medium. There exists a primary heating network that distributes hot water to hundreds (or more) heating substations and a secondary heating network that delivers hot water to end-users. High-temperature water is circulated in the primary circuit to deliver heat to heating substations. At the heating substations, the water from the primary loop is heated by plate heat exchangers for X. Lin (B) · J. Chen · W. Huang · W. Zhong College of Energy Engineering, Zhejiang University, Hangzhou, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 T. Zhou et al. (eds.), Smart Buildings and Technologies for Sustainable Cities in China, Urban Sustainability, https://doi.org/10.1007/978-981-99-6391-1_7

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Fig. 1 Illustration of UCHS in northern China [12]

the secondary loop. The secondary loop further provides hot water to the end users. The end-users are residential communities, and public buildings. Along with rapid urbanization, the scale of UCHS in China is expanding in size. The discharge of pollutants leads to serious environmental problems. And the operation of complex large-scale systems faces many challenges. In 2015, the total length of the pipe network exceeded 200,000 km, covering a heating area of 8,500 million m2 [9]. UCHSs are located in urban areas of northern China cities. UCHSs in megacities could cover tens to hundreds of millions of square meters. For example, Beijing’s central heating system covers more than 600 million m2 of floor area. It is extremely challenging to ensure the safe, reliable, energy-efficient, and environmentally benign operation of these heating systems. According to Fang et al. [3], urban and rural heating in northern China is dominated by combined heat and power (CHP) plants and coal-based boilers, with coal-based boilers accounting for 81% of the total heating area. Zhang [29] reported that half of the coal is consumed through inefficiency and found that pollutant emissions from inefficient combustion are the main cause of fog and haze in northern China. Conventionally, the difficulties of UCHS operation control come from the following aspects: ● The difficulty of coordinating control among multiple heating sources with different characteristics.

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● Thermal inertia and corresponding system response delay. ● Coupling effects of heating network topological structures (pumps, valves). Conventional UCHS systems have difficulty finding new operating points in scenarios outside of their design conditions. Over the past decade, researchers have observed the following trends in UCHSs in China: ● Increasingly, clean coal-based CHP plants are being used as the major heating source while adopting renewable-energy-based heating sources. It is necessary to separate heat and power in existing CHP plants, as shown by Wang et al. [26]. This trend is also known as the clean heating and “blue sky” initiative. ● It requires that the reliability of UCHS is ensured and balanced through multienergy complementary operation, as found by Dimoulkas et al. [2].

2 UCHS Operation Control Development and Cyber-Physical Systems While researchers highlight the impact of renewable-energy-based heating sources and heating networks with high efficiency, the operation control technologies which are required to achieve such stable and high-efficiency operation of future heating networks are less discussed. It should be especially noted that the existing operation control studies pay little attention to the operation control of UCHS with ever-increasing scale and a variety of heating sources such as renewable energy. Renewable heating sources such as waste heat recovery and heat pumps could be spatially distributed or temporally unstable. Such increased complexity of UCHS makes the operation control challenging. Such increased complexity is part of the ongoing transition of heating systems focusing on low carbon heating and integration of renewable energy sources. Lund et al. [13] reviewed and discussed the “Fourth Generation District Heating” widely used in Nordic countries and found that the core concepts consisted of lowtemperature heating networks, renewable-energy-based heating sources, waste heat recovery, and the so-called “smart energy systems.” Such a concept was further illustrated in their following work, where Østergaard and Lund [18] presented a technology option for the transition of energy supply from predominantly fossil fuels to locally available renewable energy sources. Utlu et al. [23] further developed an experimental thermal investigation in terms of a hybrid renewable heating system and used real data obtained from a prototype structure to analyze the exergy efficiency. One of the difficulties of utilizing renewable energy together with conventional energy for these future heating systems is the operation control, which constitutes the basis for a “smart energy system.” Numerous studies have been carried out in this field. For instance, He et al. [8] discussed the volatility and uncertainty in intermittent renewable energy systems and proposed to optimize the scale of the clean heating and capacity of electric heating devices as well as thermal storage devices by using an optimal planning model. Wang et al. [26] studied a district heating system based

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on CHP with renewable energy sources and an energy storage system by developing its modeling and optimization method used in planning and operating. Dimoulkas et al. [2] coordinated the short-term operation model of a district heating system to optimally schedule the production of heat and power in a system with high wind power penetration. Vigrants and Blumberga [24] developed a district heating system calculation model to calculate the optimal flow and temperature design conditions in the heating network. Similar to other energy system research areas, modeling and optimization play critical roles in solving the coordination of renewable and conventional energy for future heating systems. Such modeling also drives the so-called model-based optimization, load forecasting, predictive control, or system design. For example, for the optimization model of heating systems, Nikula et al. [17] proposed a co-simulation environment for a dynamic process simulator and an event-based control system by demonstrating the effectiveness of the solution with a case study of a district heating network. In terms of economic indicators, Åberg et al. [1] constructed a cost-optimization model of a district heating system and discussed scenarios with heat demand changes due to increased energy efficiency in buildings caused by electricity price variations. For the load forecast of heating systems, Nielsen and Madsen [16] found that grey-box modeling combining an initial model structure and data on heat consumption and climate data is useful for the online prediction of heat consumption. Ferraty et al. [5] introduced a peak load forecasting methodology in a district heating system based on a functional regression approach which can support predictive control of the heating system. For heating system design, Wanjiru et al. [25] developed a closed-loop model of predictive control to operate heat pump water heaters and instantaneous showers used in zero-energy buildings. Ganchev et al. [6] presented the design and realization of an IoT-based smart electric heating control system. To further applicate in actual heating systems, Tunzi et al. [22] simulated a small-scale district heat network and presented the influence of water supply and return temperature optimization techniques on its overall performance. Haghighi et al. [7] developed a cyber physical system approach for control optimization of energy-efficient buildings in the context of smart grid. Although a lot of studies have been carried out in this field, the focus stays on the operation control of a single type of heating system. Despite these model-based studies, a systematical approach to operation control is less found, especially under the big picture of the heating system technology transition. Novel systematical concepts such as cyber-physical systems (CPS) are less touched in the field of UCHS control. CPS incorporates new-generation information technologies such as IoT, big data, cloud computing, and artificial intelligence (AI). It can address the dynamic resource allocation and optimal control problem of complex systems, although its potential is less discussed in the UCHS area. For example, Yu and Xue [27] discussed the potential impact that CPSs can have on smart grids. Rajkumar et al. [19] discussed the possibility of applying CPS to areas such as energy, transportation, manufacturing, and agriculture by discussing the challenges of developing a fully-integrated, robust, and failure-free model with distributed interacting and real-time control features with a special interest on the power grid. Faruque

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and Ahourai’s study [4] demonstrated a model-based design method for creating a “Cyber-Physical Energy System” built on a residential microgrid. Ramos et al. [20] described the use of intelligent supervisory control and data acquisition (SCADA) systems of CPS at different levels of the power system. Liu et al. [10] analyzed the basic concepts f CPS for the power grid and presented the research framework, which was made up of four key techniques. Regarding the applications of the CPS concept, researchers found the advantages of CPS in improving user behavior and realizing demand response. For example, Tham and Luo [21] found that a CPS-based smart grid with the function of sensing-driven predictions and optimization-based purchase decision-making could settle uncertainties in demand, supply, and electricity prices. Maasoumy [14] introduced CPS in the context of the smart grid that could take advantage of the flexibility of HVAC systems. The complexity of UCHS increased during the last decade, and the operation control of large-scale heating systems has been a critical challenge. Similar challenges have been dealt with in electric power systems via the approach of CPS. However, CPS is rarely involved or utilized in UCHS. One possible reason could be that conventional UCHS is sometimes sorted as a residential public service rather than an energy service. The existing UCHS operation heavily relies on human operation. However, as the percentage of clean and renewable heating sources increases, such traditional operation modes could hardly continue. From that perspective, CPS has certain control strategy advantages. Rational use of CPS in UCHS can effectively optimize operation control strategy and improve system efficiency. However, the study of integrating CPS into UCHS is relatively rare, despite its successful application in the power area.

3 CPS and Smart Heating As a systematic approach based on new information technologies, CPS has further access to the sensible and controllable elements of large-scale complex systems and could enhance the potential of efficient interconnection and resource integration, which could be reflected in UCHS as efficient integration. The big data technology in CPS could efficiently process the raw sensing data into the estimation of the heating system operation state. By using modeling and simulation techniques such as cloud computing, it is possible to map the elements of a complex physical system into cyberspace. Once the real-time mapping is established, it is possible to carry out predictive control and coordination of UCHS. The CPS technology, when applied to the UCHS system, is essentially an AI-assisted heating operation decision-making system for complex and large-scale UCHS. It also has the potential to be integrated as a foundation of a smart heating system. The CPS-based control platform interacts with the actual physical heating system through state sensing and accurate execution. Our previous work [11] has discussed how CPS could be borrowed into the area of energy systems (especially large-scale UCHS). A new paradigm of the UCHS operation control platform is thereafter

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proposed, which includes the systematic framework of CPS-based heating control of UCHS. As shown in Fig. 2, such CPS-based control platform interacts with the actual physical heating system through state sensing and accurate execution. The physical heating system, such as UCHS discussed above, could include two loops: heating primary and secondary networks. Due to the availability of the install location, state sensing in the field is carried out by various types of sensors installed on the site. It includes data such as mass flow rate, temperature, pressure, valve opening, and so on. Conventionally, these data are limited to the granularity of the heating substation. As part of the ongoing heat metering reform promoted by the Chinese government, new heat metering devices installed at the entrance of the building (in some region of China, it is further installed at the entrance of single household in apartments) also allows for a more detailed data collection. However, due to the expense of these metering devices, the fine-granularity data of UCHS is limited. In the actual site, achieving substation-wise operation data is more accessible. Despite being critical to operation, such data is insufficient to build a cyber counterpart of the physical system. The CPS-based control platform has four core parts: calibration and forecasting, heating system model and optimization algorithm, and state evaluations and load distribution. For heating system model, it depicts the coupling relationship between the measured data and the full-state operation condition of the entire UCHS. Per the nature of the CPS approach, such a model is assumed to reflect the actual physical system constantly. Therefore, model calibration based on sensing data is required to adjust the key model parameters. Take UCHS for example, to yield a satisfying model accuracy for such a CPS-based control platform, an implicit model calibration method is necessary to search the real-time operating parameters such as flow resistance factor and heat transfer coefficient. The load forecasting model gives out the desired

Fig. 2 A framework of CPS-Based UCHS control platform used in heating system

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heating required by the end-users (from either the viewpoint of secondary network substations or buildings based on the granularity of the model). Such load forecasting sets up the constraints required in the decision-making of UCHS operations. The selection of an optimization algorithm (conventionally gradient-based, derivationfree, or metaheuristic) is critical to the optimal operation of such a system. Although numerous methods have been proposed for energy system optimization, it should be noted that the optimization in the CPS-based control platform is slightly different. As noted, the model itself is changing. For generality, it is suggested to use a natureinspired metaheuristic algorithm. However, the drawback of such an approach is that it is difficult to ensure the stability of the achieved optimal operation solution. The achieved solution must be translated into the “language” the field controllers understand. Such a process then includes the decomposition of optimal operation solutions, such as multi-heat source load distribution, into the detailed operation commands. Such commands will be transmitted into the DCS system and down to physical components (such as pumps, valves, and heating sources). Such a decisionmaking and operation control process iteratively repeats itself. From the perspective of UCHS, it undergoes a series of “data sensing-calculation-optimization-actuation” workflows. Based on the proposed framework, it is now possible to find out the role of a CPSbased control platform in the scope of UCHS. The role of such a platform is shown in Fig. 3. The conventional UCHS control, to a large extent still in China today, is an operation scenario where the CPS-based control platform is missing. The operation of UCHS highly depends on an empirical process. For instance, substation weather data models, which reflect the desired water inlet temperature of the substation and the ambient condition, are being introduced into the daily operation in some largescale UCHS companies. However, the weather data models are an empirical summary of historic data observed in the substation. Moreover, from the desired water inlet temperature to an expected valve opening or pump frequency, the final step of operation control in the substation might be carried out by manual operation. In fact, by looking at the structure of human resources available on site, there is no clear difference between operator and maintenance worker. Once the predictive analysis is required in the operation control, such an existing methodology could hardly be satisfactory. The essential part in Fig. 3 is to bestow the conventional UCHS system with new abilities that could be found in the CPS-based control platform detailed in Fig. 2. To achieve such a vision in UCHS, the requirements lay in the combination of automation system and the Internet of Things (IoT) technology. Cheaper IoT devices and affordable automation devices could highly support the exchange of state sensing and action execution between the physical and the cyber system. The effect of a CPS-based control platform could be illustrated in three examples carried out in our previous work. The readers are encouraged to refer to the related work [28, 31–34] to find more details regarding this issue. Based on the CPSbased platform, operation control problems of UCHS like heat delay time quantification, heating substation primary loop valve control and large-scale UCHS operation control will be explained in the following pages.

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Fig. 3 Role of CPS-based platform in UCHS

3.1 Heat Delay Time Quantification The heating delay is a widely observed phenomenon in UCHS, but there are few investigations on heat delay in UCHS based on data-driven methods. One challenge is that the delay time is affected by operating conditions. However, it is possible to achieve a quantitative analysis of delay from the topology of UCHS. The actual delay time on site is highly affected by the scale of the system and its operating condition. A CPS-based platform allows the creation of new features upon the same basis of data: field-collected and model-calculated data. Our previous work [32, 34] proposed automatically obtaining the heat delay time based on change-point identification, time window slide, and correlation analysis. The quantitative analysis of delay time could contribute to system operation in the following two aspects. First, it adds to the feature of CPS in UCHS. It is now possible for UCHS managers to get a comprehensive view of the heat delay time of all substations automatically and accurately. The demonstration and analysis of delay time are critical to heat dispatch among the substations in large-scale UCHS. The difference in heat delay was considered a negative factor in UCHS management since it causes a lag between heating system decision-making and its expected outcome. However, once the delay time is quantified and sorted, it is now possible to categorize the substations into different ranks, creating new degrees of freedom for operation. Second, the significant factors affecting the delay time can be better understood by correlation analysis and linear feature fusion. Feature fusion and delay time prediction can be possible for projects with limited resources with simple models such as supporting vector regression. Such HRT identification and prediction method can be applied to UCHS and various types of district energy systems. It provides the research foundation for the system research of large-scale energy systems where delay becomes a critical issue.

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3.2 Heating Substation Primary Loop Valve Control Large-scale UCHS relies on substations to deliver the heat to end-users. These endusers include various types of buildings: hospitals, households, schools, and public and commercial buildings. It should be noted that the term “household” of Chinese UCHS here in this context is similar to the residents in apartments in Europe or the United States. As a rule of thumb, the covered heating area of single heating substations could range from 50,000 to 250,000 m2 . Although the major UCHS owners could maintain their own substations regularly, the operation control of substations is still a critical challenge in UCHS. Moreover, the hardware and software configurations in heating substations vary from one city to another. Most substations’ existing operation control is manual, leading to either overheating or insufficient heating. Although it is possible to carry out mechanismbased modeling for a single heating substation and its covered end-users, it is impossible to ensure these models’ scalability and feasibility. In our previous work [27], we developed the operation control strategy of PLVO in heating substations based on data-driven models. The reason behind such a data-driven approach is to ensure the model’s generality since it is easier to plug it into existing heating substation operation and data collection devices. The developed strategy is encapsulated as a special module in the cyber layer of UCHS. The method was applied to a demo site in Zhengzhou City, Henan Province. The onsite demonstration shows that such an approach has the potential to reduce overheating and avoids insufficient heating while ensuring indoor thermal comfort.

3.3 Large-Scale UCHS Operation Control The developed CPS-based control platform shows sufficient accuracy for pilot test use, and the practical case proves that CPS-based operational control saves energy consumption and reduces heating costs. We used a UCHS adjacent to Beijing as an example in our previous work [28, 31, 32, 34] to demonstrate the effectiveness of implementing the control platform. 88.5% of the modeled substation supply pressure has an error of less than 5%, and 82.1% of the modeled substation return pressure has an error of less than 5%. We test the developed platform’s multi-sources load distribution optimization, where particle swarm optimization is used to solve complex multi-objective nonlinear programming problems in UCHS. During a two-day pilot operation in December 2017, the results from the study showed that under certain conditions, CPS-based operation control reduced natural gas consumption by up to 31.2% and reduced the total heating cost by 2.6% compared with conventional operator experience-based control. From these examples, it is possible to summarize features that distinguish smart heating control based on CPS from conventional operation control. Such a comparison is shown in Table 1. Table 1 lists three major features: system structure, control

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Table 1 Comparison between CPS-based smart control and conventional control in UCHS Features

Traditional heating control

Smart heating control

System structure

Multiple independent systems correspond to different functions

An integrated system design with functions integrated

Control framework

Relying on onsite local controllers Carrying out integrated operation control with little unified coordination considering the whole process of “source-network-load-storage” in UCHS

Control mode

Taking “day”/“hour” as the basic interim to carry out long-term dispatch planning; Less attention is paid to operation optimization

Taking “minute”/“hour” as the basic interim to carry out short-term predictive control; Optimized decision-making based on digital twin models

framework, and control mode. As can be found in Table 1, the key differences between smart control and conventional control in UCHS are the role of model and modelbased operation. As UCHS grows, the composition of heating sources and network scale increases, leading to a more complex system. Conventionally empirically operated, next-generation UCHS operation control needs to be quantified and optimized, highlighting the role of the model in this transition.

4 Smart: From Heating to Integrated Energy Systems Although the UCHS system is the central topic of this chapter, it should be noted that the future development of UCHS relies on the heating system itself and other energy carriers (such as natural gas, steam heating, electricity, and so on). Novel energy systems such as district energy systems, distributed energy systems, multi-carrier energy systems, and integrated energy systems have been proposed. Regardless of the name, it appears in different areas of energy science. These systems focus on the integration of multiple energy carriers. For example, in district energy systems (sometimes called combined cooling and heating systems), cooling and heating are integrated into the same energy supply and consumption vision. In multi-carrier or integrated energy systems, electricity is combined with heating and natural gas to provide a complementary energy supply structure. We will briefly use an example discussed in our previous work [30] to show the effectiveness of the CPS-based approach, which should be a reasonable extension of previous CPS-based smart heating. Our previous work used a blower manufacturing industrial park located in Xi’an, Shanxi, China as a demo case. As shown in Fig. 4, the case industrial park has a ground source heat pump, natural-gas-based combined cooling and heating, waste heat recovery, photovoltaic, and storage tanks. To ensure the safe operation of the park, the park builds a microgrid with a connection to the public grid. The details of the energy system in the park are shown in Table 2. The heating and cooling demand comes from the workshop and its affiliated office buildings. The heating

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and cooling demand is the hot water or chilled water demand. It should be pointed out that although the case park doesn’t have another heating demand (the steam demand), it is common to have stem demands in chemical and pharmaceutical parks. In these parks, steam is provided by combined heating and power plants rather than standalone steam boilers. The case park could achieve enhanced operation by reducing the ramp-up time of electricity-driven equipment such as heat pumps and water pumps. Such enhanced operation control reduced the daily energy cost of the system. Such operation requires the prediction of the demand, the modeling of the system, the scrolling correction of the model, and a day-ahead scheduling operation. These features are similar to those of smart heating shown in Fig. 2. Since the framework of CPS-based operation control could be extended by having more elements (from cooling, electricity, and so on) in the scope of the energy system. Although the discussion in this chapter takes an integrated energy system as an example, it should be noted that CPS should also be feasible for these listed energy systems.

Fig. 4 An integrated energy system case in an industrial park and its CPS-based operation platform

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Table 2 Case industrial park integrated energy system key parameters Key parameters

Description

Heating/cooling demand

Heating/cooling of 11,4000 m2 manufacturing workshops and its affiliated office building

Natural gas

Consumption of 771,000–143,800 m3 /year

Electricity demand

Pump and compressor power demand

Ground source heat pump

Rated power consumption 575 kW; Rated heating capacity 1995 kW

Gas-fired hot water boiler

Rated heating capacity 4.2 MW

Energy storage tank

Volume 950 m3

5 Challenges and Outlook This chapter discusses the key concepts behind such a new framework of operation control in UCHS: CPS structure, its key components, and its paradigm. As far as the authors are concerned, the cornerstone of the model in the sense of CPS is its granularity and its constant accuracy. The granularity shows to which extent the model (the twin) reflects the physical system. The constant accuracy set up the basic validity required in the “smartness” layer, such as operation control, forecast, and decision-making. Three examples are provided to show the effectiveness of this CPS-based approach. Such an approach also comes with challenges. For example, the developed platforms found in existing studies are still limited in terms of data selectivity and processing. It is known that experienced human operators could deal with both structured, standard data such as temperature, pressure, and flow rate and unstructured/stochastic data such as thermal comfort and potential faults. It should be interesting to see how the analysis of CPS-based modules could be fused with the subjective decision of human beings. Such fusion could lead to better coordination of explicit and implicit aspects of ever-growing UCHS, allowing for a “fuzzy” or flexible decision-making, to make more comprehensive and responsive decisions. The research scope spans heating to electricity and smart heating to smart energy technologies. Consequently, the cyber part of the CPS needs to be extended. Conventionally, either mechanism-based or data-driven models are used in energy science. However, to ensure the granularity and accuracy of the model (especially when the system evolves into an integrated energy system), both approaches need to be combined. Overall, the introduction of CPS into UCHS is not only critical to the operation control of UCHSs with increasing complexity. As UCHS evolves into an integrated system with various types of renewable energy and storage systems in the future, such a concept is essential to the design, planning and operation of the integrated system. Acknowledgements This work is supported by the Natural Science Foundation of China (Grant No. 51806190) and the National Key R&D Program of China (Grant No. 2019YFE0126000).

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The Application of n-D BIM in Chinese Construction Projects Baotian Chang, Byung Gyoo Kang, and Nan Zhang

Abstract With the introduction of the concept of n-DIM and the advancement of research, the application of BIM in the architecture, engineering and construction industries, engineering and construction industries has been widely recognized, but it has not been clearly and deeply studied in construction projects in China. This chapter is concerned with studying the application of n-D BIM in China, from 3D BIM to 7D BIM. Firstly, based on literature research, this paper outlines some related concepts such as n-D BIM and SWOT analysis. Secondly, a Tencent Questionnaire is used for online distribution and collection, and SPSS 25.0 is used for descriptive analysis, difference analysis, multiple response analysis, etc. Finally, the strengths, weaknesses, opportunities and threats of n-D BIM in China’s construction projects are determined by SWOT analysis. Through cross-analysis, a series of strategies for promoting the development of n-D BIM are obtained. The results indicate that the recognition of n-D BIM application in Chinese construction projects is at the lowermiddle level, poor overall utilization, insufficient management engagement with the software and a lack of supporting conditions. Visualization, high implementation cost, unified promotion and lack of BIM training are the four primary strengths, weaknesses, opportunities and threats of n-D BIM at present. Keywords n-D BIM · SWOT analysis · Questionnaire survey · Construction industry

B. Chang · N. Zhang Beijing University of Technology, 100 Pingleyuan, Chaoyang District, Beijing, China B. G. Kang (B) University of Nottingham Ningbo China, 199 Taikang East Road, Ningbo, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 T. Zhou et al. (eds.), Smart Buildings and Technologies for Sustainable Cities in China, Urban Sustainability, https://doi.org/10.1007/978-981-99-6391-1_8

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1 BIM and n-D BIM BIM is the acronym of building information modelling. The concept of BIM was first put forward around 50 years ago. As such modelling has evolved, many organizations or institutions have made similar definitions of BIM. For instance, the Institution of Civil Engineers (ICE) and the Chartered Institution of Civil Engineering Surveyors (ICES) have the following definition of BIM: BIM is the management of information through the whole lifecycle of a built asset. However, many people have a misconception about BIM, thinking that it is only software used in the construction process. In fact, BIM cannot be simply defined as a piece of software or a tool. It delivers value by underpinning the creation, collation and exchange of shared models and corresponding intelligent structured data. No matter how BIM is defined, BIM-related research and application are becoming more and more mature. Even in China, where BIM started late, it has been applied in many different types of projects, including railway engineering projects [21] and waterway engineering projects [18] to name a few. Likewise, BIM has been widely used in many largescale construction projects, such as Shanghai Tower and Zhuhai Grand Theatre. The application value of BIM in construction projects is mainly reflected in the integrated management of building information [19], detection of clashes between pipelines and structures [20], and many other aspects. As such, the application of and research on BIM have attracted much attention. With the development of BIM research and application, many scholars have proposed the concept of n-D BIM. Generally speaking, there are two main interpretations of n-D BIM: on the one hand, n-D BIM refers to BIM models of different dimensions [10], which are analyzed dynamically and virtually. On the other hand, n-D BIM means a dynamic multidimensional BIM database containing construction records [12]. In short, n-D BIM is an extension and expansion of 3D BIM. On the basis of construction project information such as schedule, cost, sustainability and maintenance, n-D BIM can better realize the dynamic management and interconnected utilization of construction projects. Generally speaking, n-D BIM can be divided into a range from 3D BIM to 7D BIM according to different dimensions and different emphases, which respectively refer to geometric information (3D BIM), time (4D BIM), costs (5D BIM), environmental sustainability (6D BIM) and facility management (7D BIM) [11]. Compared with BIM, n-D BIM emphasizes the lifecycle approach of BIM in the project.

1.1 Current Status of n-D BIM in Other Countries In recent years, foreign scholars have obtained many valuable research results from practical application of n-D BIM. Lee et al. [6] noted the details involved in n-D BIM. In their research, a 3D BIM model partitioning system based on user requirements specification was developed to solve the problem of modelling information in n-D

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BIM. Fuchs and Scherer [2] investigated geometric data exchange in n-D BIM, and proposed a multi-model method based on original data to deal with construction information processing. Häußler and Borrmann [4] put forward a series of quality parameters suitable for infrastructure planning of construction sites, and focused on the clash detection in n-D BIM, especially 3D BIM and 4D BIM. Johnston et al. [3] used virtual prototyping to exchange and share constructability information, thereby emphasizing the potential of n-D BIM in architecture, engineering and construction (AEC). It can be seen that the research on n-D BIM by scholars from all over the world not only focuses on its application value, but is also beginning to consider its influence and limitations among other aspects.

1.2 Current Status of n-D BIM in China The rapid development of China’s economy, scientific and technological progress guarantees and provides strong support for the application and development of a series of cutting-edge technologies such as BIM. In order to solve conflict when installing prefabricated components in construction projects, Wang et al. [15] proposed a method based on 4D BIM, which realized the prevention and detection of workspace conflicts through simulation, thus improving the project quality. Li et al. [7] made a case study of Haicang Tunnel and found that during the construction process, the constructors could simulate the progress of time and cost through 4D BIM and 5D BIM. Likewise, Liu et al. [8] demonstrated the application of 5D BIM in project management. Their research shows that 5D BIM is an established tool in construction management, and can provide technical support for key and difficult issues. The above research suggests that the application and research on n-D BIM in China mainly focus on 4D BIM and 5D BIM. That is to say, in China, more and more construction projects have used BIM for schedule simulation and cost control, and the application value of n-D BIM to time and cost in the construction process is gradually being recognized by the public. However, the application of n-D BIM in China’s construction projects and the improvement of its popularity still require the mutual cooperation and collaboration of domestic scholars and relevant practitioners.

2 Cognition and Application of n-D BIM in China To investigate the practical application of n-D BIM and the perceptions of construction professionals in China, we conducted a survey on n-D BIM application, and the sample sources of this survey were all employed in the fields of architecture, engineering and construction, including architects, engineers, project managers and cost engineers to name a few. The questionnaire used in the study can be divided into three parts. The first part of the questionnaire was mainly used to obtain some sample distribution information

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Table 1 Basic information Distribution mode

Distribution platform

Total number

response rate (%)

effective rate (%)

Online

Tencent Questionnaire

214 Questionnaires

46%

100%

about respondents, including age, educational background, profession, years of using BIM and other attributes. As the core content of the questionnaire, the second part was composed of five matrix scale questions, which were used to investigate the application of 3D BIM to 7D BIM in current construction projects in China. The last part of the questionnaire is about the driving factors and problems that arise in the application of n-D BIM. The questionnaire passed the reliability and validity test, and the survey strictly abided by ethics considerations. The basic information of the survey is shown in the following Table 1.

2.1 Distribution of Firms and Professions of Respondents The distribution of respondents’ firms and professions can be arranged as the following pie chart (Fig. 1). From the above results, it can be seen that most respondents’ firms operate in the fields of design, investment and construction, of which 97 are from construction firms, accounting for almost half of the survey results. The reason for the small number of respondents from consultancy and supervision firms may be that BIM consulting firms are still in the initial stage of development in China, especially in underdeveloped cities. The management of these consulting firms may therefore currently pay insufficient attention to BIM.

Fig. 1 Distribution of respondents

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In addition, it can be seen from the survey results that the respondents cover nearly all professions in the industry in roughly equal proportions. According to the survey data, architects, engineers, cost engineers and construction managers each account for about 20% of the total sample, among which engineers are composed of civil engineers, structural engineers and MEP engineers. Architects and engineers not only play an important role in construction projects, but also have a deep application foundation of BIM. For this reason, we will analyze and discuss the differences between architects and engineers on n-D BIM in particular.

2.2 Respondents’ Knowledge of BIM The results after data processing are shown in the following Fig. 2. It can be seen from the pie chart that more than half of the respondents have chosen ‘Neutral’ to describe their knowledge of BIM, which indicates that BIM is well known by most people, especially the young generation in the industry. However, the data shows that there are still eight people who know nothing about BIM. The respondents’ knowledge of BIM can provide a favorable basis and point of reference for future research on the development trend of n-D BIM. Fig. 2 Distribution of respondents

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Table 2 Distribution of working and applying BIM years Number of people

Percentage (%)

0–5 years

160

74.8

6–10 years

33

15.4

11–15 years

4

1.9

15–20 years

6

2.8

Items Working life

Years of applying BIM

20 years or more

11

5.1

Not used before

89

41.6

Applied for less than 1 year

80

37.4

Applied for 1–2 years

38

17.8

Applied for 3–5 years

5

2.3

Applied for more than 5 years

2

0.9

2.3 Years of Working and Applying BIM Analyzing the data from groups with different amounts of professional experience can not only increase the diversity of data, but also provide more references for analyzing the current situation of n-D BIM application. According to the data in Table 2, the working lives of the respondents in the questionnaire survey are mainly concentrated in ten years or less, accounting for more than 90% of those surveyed, and most respondents had less than five years’ experience. There is no doubt that these newcomers should have a higher acceptance and learning ability, and their attitudes and viewpoints can better reflect the future development trends of architecture, engineering and construction fields. Therefore, the research on the current adoption of n-D BIM based on the younger people in the industry is of great value. Nevertheless, from the data of BIM usage years, it can be seen that the number of people who have used BIM for less than one year accounted for almost 80% of all respondents. This shows that in the fields of construction, engineering and construction in China, even if most people have some knowledge of BIM, its use in actual work has not been optimized yet. The practical application of n-D BIM in construction projects will be analyzed in detail below.

2.4 Analysis of Current n-D BIM Usage We use descriptive analysis to investigate the application status of n-D BIM, that is, the average value is used to reflect the statistical results of the current situation. The statistical results are based on all respondents. The following Table 3 shows the statistical results. Based on the setting of the Likert scale, the values ‘1’ to ‘5’ in this study correspond to ‘Strongly disagree’, ‘Disagree’, ‘Neither agree nor disagree’, ‘Agree’ and

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Table 3 Descriptive statistics of current situation Attributes

N

Min

Max

Mean

S.D

3D BIM application status

214

1.00

5.00

2.7864

1.10871

4D BIM application status

214

1.00

4.60

2.5121

0.99470

5D BIM application status

214

1.00

4.83

2.0841

0.76175

6D BIM application status

214

1.00

4.40

1.5710

0.58887

7D BIM application status

214

1.00

4.33

1.3785

0.56274

Valid items

214

–

–

–

–

‘Strongly agree’ respectively. Generally speaking, 214 respondents have not reached a medium level of recognition for the application status of n-D BIM in construction projects. Specifically, the recognition degree of 3D BIM application is the highest, with an average value of 2.7864, indicating that the application of 3D BIM in construction projects is the most effective among n-D BIM, which is basically satisfactory. However, the recognition degree of 7D BIM is the lowest, with an average value of only 1.3785, which shows that 7D BIM has hardly been applied in construction projects. Additionally, it is obvious from the data that with the increase of BIM dimensions, its application in construction projects becomes less frequent, which is consistent with the n-D BIM research trends. In short, the application value of 3D BIM is basically mature, but the application research of 7D BIM is still in the exploratory stage.

2.5 Difference Analysis We use the methods of one-way ANOVA and pairwise comparison to conduct the difference analysis of the identification with n-D BIM application from the perspective of different professions. According to the significance results of pairwise comparison, there are obvious differences among architects, engineers, cost engineers and construction managers. Civil engineers have been taken to act as representatives of engineers in general and their responses are contrasted with those of architects, cost engineers and construction managers in a comparative analysis of survey data. The results of the difference analysis are shown in the histogram below (Fig. 3). It can be seen from the histogram that with the increase of BIM application dimensions, recognition of the application status of n-D BIM in construction projects is reduced, except in the case of construction managers, which indicates that most respondents believe that the application status of 3D BIM and 4D BIM is currently superior to that of 6D BIM and 7D BIM. Nevertheless, for construction managers, the data shows that the application of 4D BIM in the construction project is the most satisfactory, which means that compared with other BIM application values, the application points such as schedule control or construction simulation brought by 4D BIM play an effective role on the construction site.

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Fig. 3 The difference analysis of professions

Moreover, it can be clearly seen from the figure that architects have given the highest evaluation of BIM application in most different dimensions, while compared with other professional groups, construction managers hold a relatively negative view on the application of n-D BIM. This implies that the overall application status of BIM on construction sites is not considered satisfactory. Thus, it is very important to strengthen the cooperative application of n-D BIM in different stages of construction projects. Finally, the data demonstrates that different professional groups have different views on the application of 3D BIM, but even if there are some differences, 3D BIM is still the most satisfactory BIM application aspect for most respondents in these four professional groups. On the other hand, architects, engineers, cost engineers and construction managers have almost identical negative comments on the application status of 6D BIM and 7D BIM. As a consequence, the application and research of 6D BIM and 7D BIM need further exploration in the future.

2.6 Multiple Response Analysis The last two questions in the questionnaire are multiple-choice questions about the driving and hindering factors of n-D BIM in construction projects, aiming at investigating the incentive factors of respondents applying n-D BIM in practical projects and the problems faced in the application process. In this paper, SPSS 25.0 is used to analyze the data from these two questions in multiple responses. In the process of data processing, the driving factors of n-D BIM application in the project are

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defined as ‘Factor l’ to ‘Factor 7’, and ‘Factor A’ to ‘Factor G’ are used to indicate the problems existing in the application of n-D BIM (Table 4). The data after multiple response analysis can be expressed by a histogram (Fig. 4). Regarding the driving factors of n-D BIM in the project, the government and firms’ promotion or encouragement policies for n-D BIM are the two primary factors selected by respondents. This shows that the external or environmental factors for the application of n-D BIM are relatively mature and very beneficial to the future development of n-D BIM. Except for ‘Others (Factor 7)’, personal career development demand holds the lowest response rate among all the driving factors, which indirectly suggests that most respondents think that BIM application is not related to personal career development. At the same time, ‘The traditional way cannot meet the needs of the project’ is another factor with low response rate, only 40.7%. The Table 4 Driving factors and hindering factors of n-D BIM Content

Item Driving factor

Hindering factor

Factor 1

Unified promotion of BIM by firms

Factor 2

Government’s encouragement policy

Factor 3

Definite requirements of the client

Factor 4

Traditional methods cannot meet the needs of the project

Factor 5

N-D BIM has obvious advantages in project management

Factor 6

Personal professional development

Factor 7

Others

Factor A

Lack of or unclear management framework

Factor B

Nonstandard work flow

Factor C

High implementation cost

Factor D

Data loss or deviation in software interaction

Factor E

Complex operation of related software

Factor F

BIM personnel lack corresponding training

Factor G

Others

Fig. 4 The multiple response analysis of driving and hindering factors

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result suggests that the methods currently used in Chinese construction projects are seen by many as satisfactory at present, and there is still a long way to go for the deep application of n-D BIM in Chinese construction projects. However, with the increasing complexity and modernization of construction projects, the limitations of traditional construction methods will gradually become apparent. In terms of the problems existing in the application of n-D BIM, the choices given by respondents are relatively consistent. Except for the option ‘Others (Factor G)’, the response rate of each item is about 50%, among which the high implementation cost is considered the biggest problem, and the complicated software operation and the lack of BIM training are other significant issues in practical application. On the basis of multiple response analysis of two multiple-choice questions, we introduced the response differences between architects and engineers to conduct a chi-square test, so as to explore analyse the differences between architects’ and engineers´ responses when applying n-D BIM in actual projects. The engineers’ data is the sum of the data of civil engineers, structural engineers and MEP engineers. The data shows that the significance between the problems existing in the application of n-D BIM (hindering factors) and the two professions is less than 0.05. That is, there is a significant difference between the views of architects and engineers. In view of this difference, this paper presents the data of both sides in a visual way (Fig. 5). Through the histogram, obvious differences can be found, especially with regard to the perceived absence of management framework (Factor A), the irregularity of workflow (Factor B) and data deviation of software interaction (Factor D). Specifically, the biggest concerns for surveyed architects were data loss between software and specific operation problems, while engineers paid more attention to management framework and workflow in the process of applying n-D BIM. This shows that

Fig. 5 Chi-square analysis of hindering factors

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architects and engineers have different emphases on construction projects, so they have different considerations when applying n-D BIM.

3 SWOT of n-D BIM in China SWOT analysis was proposed by an American management scientist in 1980s and it is widely used to formulate enterprise development strategy and industry strategy. Through the cognition of internal and external resources and characteristics, SWOT analysis can help managers make strategic plans and provide a framework for important decisions in related fields [13]. To put it simply, to conduct a SWOT analysis is to list the strengths, weaknesses, opportunities and threats of the research object according to the ranking method of importance and influence on the premise of fully digging into the internal and external factors of the research object. In this article, the SWOT analysis method is used to study the application status of n-D BIM in construction projects in China. SWOT analysis is often used in the study of enterprise or economic management, but rarely used in the development of the construction industry. The research method is based on literature research and survey, and summarizes the strengths, weaknesses, opportunities and threats of the current application of n-D BIM by analyzing important factors. According to the findings obtained from literature research, combined with a series of documents and national policies, the strengths, weaknesses, opportunities and threats of n-D BIM are summarized in the following Table 5. Table 5 SWOT of n-D BIM application based on literature review Content

Item Strength

Weakness Opportunity

Threat

S1

Integrated management data or materials

S2

Can improve the quality and efficiency of project management

S3

Convenient communication and collaboration among stakeholders

W1

High implementation cost

W2

Poor interaction between software

O1

Unified driving force of the firms

O2

Government’s encouragement policy

O3

Increasingly required by clients

T1

Lack of relevant training

T2

Lack of uniform norms or standards

T3

Certain risks exist

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3.1 Strengths Integrated Management Data or Materials In contrast to the scattered data resources in traditional projects, n-D BIM can manage the building data or information in projects in an integrated way. COBie is a standard for building information exchange in project construction, which is reusable, checkable and interoperable [5]. Accordingly, COBie can lay a foundation for the integrated management of information in n-D BIM. Moreover, 5D BIM can realize high integration of 3D models with cost and schedule information. It Can Improve the Quality and Efficiency of Project Management In general, n-D BIM can give full play to the advantages of project management on the construction site and in the back office. Specifically, 4D BIM can assist construction site management by optimizing construction organization design. 5D BIM can strengthen the quality and efficiency of project management with a series of advantages in cost, including rapid and accurate extraction of engineering quantities, dynamic cost analysis and real-time control [9]. Convenient Communication and Collaboration Among Stakeholders N-D BIM emphasizes the application in the whole lifecycle of construction projects. As such, all participants in the project, such as client, contractor and subcontractor, can carry out relevant work with the assistance of n-D BIM. Work efficiency, communication and cooperation between different teams can be significantly enhanced.

3.2 Weaknesses High Implementation Cost N-D BIM entails higher requirements in software and hardware. Because the file size of a BIM model is always quite large, the hardware equipment in the project needs upgrading or innovation. Many small projects often give up the application of BIM because of its high implementation cost. Poor Interaction Between Software The poor interaction between software is the problem emphasized by most architects in the literature research. The application of n-D BIM requires different kinds of software. An unmatched file transfer format or an incomplete software family library will lead to the loss or deviation of BIM model data in the process of software interaction [17], which will affect the model quality and create extra workload.

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3.3 Opportunities Widespread Endorsement by Construction Firms The unified driving force of the firms is indispensable in promoting the development of n-D BIM. With the increasingly fierce competition in the construction market and the accelerated development of construction industrialization, most firms, especially in relatively developed cities, will provide sufficient support for the application of n-D BIM to enhance their competitiveness. Government Support Policy Since BIM was introduced in China, the government has maintained a positive attitude towards BIM applications. In recent years, the municipal governments of Shenzhen, Shanghai and other cities have issued relevant incentive policies to promote the application of BIM (Wang 2018). As a consequence, the application of n-D BIM in China’s construction projects has obvious external advantages. Increasingly Required by Clients In the bidding stage, the application of n-D BIM in the project can increase the competitiveness of the project. In addition, the client often requires the application of n-D BIM in construction projects, so as to ensure the promotion and overall quality of the project.

3.4 Threats Lack of Relevant Training Lack of BIM training is the main external factor that affects the application of nD BIM in construction projects. For practitioners, the application of n-D BIM is highly demanding in terms of the requisite theoretical and technological knowledge. Insufficient talent supply often leads to inadequate implementation of BIM-related work, and problems in the completion and handover of work. Lack of Uniform Norms or Standards During the practical application of n-D BIM in construction projects, most construction managers indicated that BIM workflow was not standardized. At present, there is a vicious circle between nonstandard workflow and insufficiently deep application of n-D BIM, that is, the application of n-D BIM is not deep enough to form a unified standard, and nonstandard workflow also limits the development of n-D BIM.

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Exist Certain Risks Innovation and change are not always smooth sailing and need to be viewed objectively. In the initial stage of n-D BIM application, there are potential risks in investment costs, the necessary degree of cooperation between stakeholders and complex software interaction. In the external environment, various unknowable factors will inevitably lead to disputes. For this reason, it is necessary to prepare for potential risks and formulate corresponding strategies. A second SWOT analysis is shown in the following Table 6, which is based on the literature review and survey. We will elaborate on the strengths, weaknesses, opportunities and threats based on the survey, and the other SWOT analysis of n-D BIM application has been demonstrated in the previous article. As for strengths, in addition to the previously analyzed integrated management data, superior project management and communication, visualization is the primary strength of n-D BIM in the lifecycle of Chinese construction projects based on the questionnaire data. The data show that the application dimensions of 3D BIM and 4D BIM are the most satisfactory due to the in-depth application of visualization. In the projects applying n-D BIM, visualization is not only reflected in the visual display of design results or the availability of walkthrough videos, but also in the schedule and construction simulation of 4D BIM. Therefore, the visualization of the project can be greatly improved through the application of n-D BIM. Table 6 SWOT of n-D BIM application based on literature review and survey Item Strength

Weakness

Opportunity

Threat

*

Content *S1

Improve 3D visualization of projects

S2

Integrated management data or materials

S3

It can improve the quality and efficiency of project management

S4

Convenient communication and collaboration among stakeholders

W1

High implementation cost

*W2

Complicated operation

W3

Poor interaction between software

O1

Unified driving force of the firms

O2

Government’s encouragement policy

*O3

Support of comprehensive contracting modes

*O4

Positive attitude of practitioners

O5

Increasingly required by clients

T1

Lack of relevant training

T2

Lack of uniform norms or standards

*T3

Too few years of applying BIM

*T4

Poor understanding of BIM

T5

Certain risks exist

Represents the new S, W, O, T in the second SWOT analysis

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In terms of weaknesses, according to the literature research and questionnaire survey, it can be seen that the high implementation cost and poor interaction between software are still the weaknesses in n-D BIM applications. Following this, the complexity of n-D BIM operation in construction projects is the next most important weakness, according to the survey results, with the response rate at 50%. Most practitioners prefer following the traditional working mode, instead of spending time and energy familiarizing themselves with new software or technology. To fully realize the application of n-D BIM, it is necessary to understand well and use a series of BIM-related software or platforms properly. As for opportunities, there is no denying that government and industry endorsements are the two most crucial external opportunities and driving factors in n-D BIM utilization, which can clearly be observed in both the survey results and the literature review. The survey shows that more than half of the contracting modes in actual projects use EPC (General Contracting for Design, Purchasing and Construction) and DB (General Contracting for Design and Construction). EPC is conducive to the overall planning and coordinated operation of the whole project. Additionally, in the process of EPC project construction, technical information is always attached to a BIM platform [14]. As such, comprehensive contracting modes such as EPC, IPD and DB can provide suitable environmental and developmental opportunities for n-D BIM application. Besides, according to the survey, although the application situation may not always be satisfactory, the respondents are still apparently looking forward to the application of n-D BIM, hence the positive attitude of practitioners is another indispensable opportunity at this stage. Evidently, the wide application of n-D BIM in Chinese construction projects may be only a matter of time. In terms of threats, too few years of applying BIM and poor understanding of BIM are serious problems exposed in the investigation. The results manifest that less than 20% of the respondents are familiar with BIM, and nearly 80% of the respondents have used BIM for less than one year, which is unfavorable to the actual development of n-D BIM. Analysis of these two influencing factors suggests that more widespread recognition of the value of n-D BIM will emerge as familiarity with and understanding of this tool increase. As a consequence, the popularization and application of n-D BIM can be strengthened by setting up pilot applications in different cities.

4 Development and Prospects of n-D BIM in China 4.1 Suggestions Based on the Findings of This Study Based on the above findings of the n-D BIM application status in China, three suggestions will be put forward in this section to promote the practical application of n-D BIM in construction projects.

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1. Adopt comprehensive contracting modes to improve the integrity of n-D BIM applications. In order to strengthen the integrity of n-D BIM application in construction projects, appropriate contracting modes are indispensable. In the contracting modes widely used in construction projects at present, IPD (Integrated Project Delivery) and DB (General Contracting for Design and Construction) are two modes suitable for n-D BIM [1]. These contracting modes emphasize that the owner is signing a contract with the ‘consortium’, that is, it can be stipulated that a contract includes the whole project team: architects, engineers and contractors. A comprehensive contract can not only support the overall application of n-D BIM to the greatest extent, and enhance communication and coordination among stakeholders, but also conform to the application characteristics of the whole lifecycle of n-D BIM. 2. Strengthen publicity and increase product awareness of stakeholders. The survey found that the government and firms have provided sufficient promotion of n-D BIM, but the unsatisfactory application of n-D BIM in construction projects still shows that some stakeholders have not fully realized the value of n-D BIM, and their awareness needs to be raised. Therefore, in order to maximize collaboration and knowledge sharing, all employees in construction projects should receive nD BIM training, which can greatly enhance the employees’ engagement with and enthusiasm for the application of n-D BIM. 3. Improving the supporting conditions of n-D BIM. The promotion and application of n-D BIM cannot be separated from the hardware and software support, nor from the cultivation of BIM expertise and workflow standards and specifications. In practice, BIM models may bring the risk of computer overload or software crashes. In addition, irregular workflow may also lead to untimely follow-up or loss of accountability. Thus, when using n-D BIM in construction projects, it is necessary to pay attention to every detail, and each participant should be clear about his or her own tasks.

4.2 Strategies Based on SWOT Analysis Combined with the above research and SWOT analysis, we propose four strategies to improve the operation of n-D BIM by expanding its advantages and opportunities and reducing its internal and external disadvantages, so as to promote the practical application of n-D BIM in future projects. 1. Vigorously develop n-D BIM in key or government investment projects (S–O Strategy). S–O strategies aim to overcome weaknesses by maximizing external opportunities and internal strengths. Undoubtedly, the potential application value of n-D BIM is outstanding, and it can inject new vitality into the construction and management of the whole lifecycle of construction projects. Additionally, the government and enterprises also perform a strong supporting role in the popularization and application of n-D BIM. Hereby, expanding and deepening the development of n-D BIM in key public- and private-led projects can not

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only improve the overall quality of projects, but also accumulate the application experience of n-D BIM, thus providing a reference for other projects. 2. Create an interoperable BIM platform to enhance user experience (W–O Strategy). W–O strategies focus on avoiding or overcoming acknowledged weaknesses as much as possible. For example, importing and exporting BIM models between different software will reduce the accuracy of the models to a certain extent, which greatly influences the experience of practitioners and the value of BIM [16]. The lack of a local BIM platform is the root cause. Therefore, while promoting the application of BIM, the government and firms should focus on developing BIM platforms. A BIM platform with strong interoperability and comprehensiveness can guarantee the integrity of BIM models, thus laying a solid foundation for the application of n-D BIM in construction projects. 3. Strengthen school-enterprise cooperation and develop BIM professionals (S–T Strategy). S–T strategies aim to utilize the strengths of a firm or product to minimize its vulnerability to external threats. Compared with other developed countries, n-D BIM started relatively late in China, and the related research investment and the number of professionals have been relatively small, which has restricted the rate of development of n-D BIM. With the increasingly fierce market competition, professional and technical talents have become the core of competitiveness. The survey results show that the lack of BIM training and professionals hinders the application of n-D BIM in construction projects. Hence school-enterprise cooperation can train BIM professionals for firms. Furthermore, firms can also provide professionals with the opportunity to enhance their own skills, thereby fully combining BIM theory with practice and promoting the application of n-D BIM. 4. Formulate or improve relevant laws and regulations (W–T Strategy). A W–T strategy needs to be treated with caution. Both the operational problems of nD BIM itself and many external risks are related to the lack of standardized technical specifications or relevant laws and regulations. Since the application of n-D BIM in construction projects involves different participants and stages, issues such as establishing accountability and penalties for sub-standard work and optimizing workflow need to be clarified in detail. The government can draw on previous project cases and solicit expert opinions to formulate unified norms or regulations, aiming at regulating and constraining the behavior of project stakeholders. Technical specifications and relevant laws and regulations should be important guarantees for the application of n-D BIM.

5 Summary Since BIM was introduced into China, it has received extensive attention from relevant practitioners and researchers. With the development of BIM research and application, n-D BIM has gradually become a major breakthrough in the construction industry. In this article, literature research, survey and SWOT analysis are used to

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study the application status of n-D BIM in Chinese construction projects, and the strengths, weaknesses, opportunities and threats of n-D BIM at present have been determined.

References 1. Elghaish F, Abrishami S, Abu Samra S et al (2021) Cash flow system development framework within integrated project delivery (IPD) using BIM tools. Int J Constr Manag 21(6):555–570 2. Fuchs S, Scherer RJ (2017) Multimodels—instant nD-modeling using original data. Autom Constr 75:22–32 3. Johnston B, Bulbul T, Beliveau Y et al (2016) An assessment of pictographic instructions derived from a virtual prototype to support construction assembly procedures. Autom Constr 64:36–53 4. Häußler M, Borrmann A (2020) Model-based quality assurance in railway infrastructure planning. Autom Constr 109:102971 5. Kumar V, Teo ALE (2021) Development of a rule-based system to enhance the data consistency and usability of COBie datasheets. J Comput Des Eng 8(1):343–361 6. Lee SS, Kim KT, Tanoli WA et al (2020) Flexible 3D model partitioning system for nD-based BIM implementation of alignment-based civil infrastructure. J Manag Eng 36(1):4019037 7. Li S, Zhang Z, Mei G et al (2021) Utilization of Bim in the construction of a submarine tunnel: a case study in Xiamen City, China. J Civ Eng Manag 27(1):14–26 8. Liu DF, Peng XP, Liu SJ et al (2017) The application of BIM5D in project management. Constr Technol S2:720–723 9. Mi WJ (2017) Dynamic analysis of project construction cost based on BIM. Technol Innov Appl 25:110+112 10. Michaud M, Meyer J, Forgues D (2021) A taxonomy of sources of waste in BIM information flows. Buildings 11(7):291 11. Montiel-Santiago FJ, Hermoso-Orzáez MJ and Terrados-Cepeda J (2020) Sustainability and energy efficiency: BIM 6D. Study of the BIM methodology applied to hospital buildings. Value of interior lighting and daylight in energy simulation. Sustainability (Switzerland) 12(14):1–29 12. Park J, Cai H (2017) WBS-based dynamic multi-dimensional BIM database for total construction as-built documentation. Autom Constr 77:15–23 13. Phadermrod B, Crowder RM, Wills GB (2019) Importance-performance analysis based SWOT analysis. Int J Inf Manage 44:194–203 14. She JJ, Zhang QX, Zhou HH (2020) Research on knowledge integration management of EPC project based on BIM. Constr Econ 01:51–57 15. Wang PF, Wang GB, Tan D (2018) Research on the adoption-diffusion and barriers of BIM technology. Constr Econ 04:12–16 16. Wang Q, Guo Z, Mei T et al (2018) Labor crew workspace analysis for prefabricated assemblies’ installation: a 4D-BIM-based approach. Eng Constr Archit Manag 25(3):374–411 17. Wang XY, Wu XG, Liu HD et al (2020) Green building analysis and evaluation of BIM model based on gbXML. Indus Constr 07:190–197+40 18. Wang YH, Liu DQ, Liu JH et al (2019) Cost management of waterway engineering project based on BIM application. Port Waterway Eng 03:154–158 19. Xu YG, Lu P (2016) Organizational integration of large-scale construction projects based on BIM. J Railway Sci Eng 10:2092–2098 20. Zhang J, Huang J, Su TY (2019) Analysis of the application value of BIM in the design of large public construction projects. Build Sci 01:45–50 21. Zhang Y, Huang CZ, Zhu C (2019) Research on the application of railway engineering project management system based on BIM. J Railway Eng Soc 09:98–103

A Review of the Shading Adjustment Occupant Behavior Model and Evaluation Method in Office Buildings Gaoxiang Chen, Jun Lu, Maycon Sedraz, and Zhiang Zhang

Abstract Windows are a weak part of energy saving in the building envelope and building occupant control behavior significantly impacts building energy consumption performance. The study of shading occupant behavior in office buildings with large windows is of great significance to building energy consumption. This paper reviews the characteristics and limitations of occupant behavior in buildings, the shading behavior of office buildings, and evaluation methods of shading behavior on building energy consumption. It also analyzes the shortage in this research field of shading occupant behavior. Finally, this paper puts forward the prospects of shading occupant behavior: Analyzing the convenience of shading adjustment with the building parameters for a better understanding of driver mechanism and the clustering of the diversity of occupants would greatly improve the accuracy of the shading behavior model. Furthermore, integrating an analysis of the shading adjustment model with energy consumption would be advantageous for assessing the energy consumption implication of shading behavior. Keywords Occupant behavior · Shading adjusting · Stochastic model · Shading behavior · Energy-saving · Energy consumption

1 Introduction At present, carbon dioxide and other greenhouse gas emissions are increasing yearby-year, which has a huge impact on global climate change and the living environment. To meet the climate and environmental dual challenges, China set a goal of arriving at the peak of carbon dioxide emissions by 2030 and striving to realize carbon neutrality by 2060 in 2020 [4]. Under the current development model, the carbon emission from building operations will peak from 2038 to 2040, with a peak G. Chen · J. Lu (B) · M. Sedraz · Z. Zhang Department of Architecture and Built Environment, University of Nottingham Ningbo China, Ningbo, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 T. Zhou et al. (eds.), Smart Buildings and Technologies for Sustainable Cities in China, Urban Sustainability, https://doi.org/10.1007/978-981-99-6391-1_9

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of about 3.15 billion tCO2 . By 2060, the carbon emission will still be 2.72 billion tCO2 [17]. It seems difficult to meet the targets of a carbon peak in buildings by 2030 and carbon neutrality by 2060 with the current carbon emission mechanism. Furthermore, in the current situation that energy consumption and carbon emissions in construction-related fields account for nearly a quarter of the whole society, the overall emission reduction target of the whole society will not be achieved. On the other hand, windows are a weak part of the energy consumption of building envelope structures, especially in the hot summer and cold winter regions. It is reported that the radiation heat conductivity of the windows is 20 times greater than that of the wall [32]. Therefore, properly adjusting the solar radiation through the windows (to minimize the heat transfer in summer and winter through the windows) is the key to the efficiency of building energy saving, which is a current national strategy. In the comprehensive work plan of “carbon neutrality”, energy-saving of the building is the focus of the plan [27]. Optimizing the shading behavior is an important technical measure for building energy-saving. Therefore, this paper reviews the development history of shading behavior models and highlights that analyzing the convenience of shading behavior would greatly improve the accuracy of the shading behavior model integrating the diversity of occupants, which can support the development of shading behavior models in the next phase.

2 Occupant Behavior 2.1 Occupant Behavior in Building Occupant behavior refers to the behavior related to building energy consumption, including the use and adjustment of air conditioning, lighting, shading, windows, equipment, etc. (citation needed for definition). In recent years, many worldwide scholars have conducted a vast number of studies on occupant behavior models with different methods (example citations of these scholars). These studies are divided into the following three main types (as shown in Fig. 1). (1) Fixed mode The fixed model set unified the timeline of the management in simulation. Although such a setting makes it easy to operate and widely used in the engineering field, this description method is a deterministic model, which cannot show the stochastic behavior of the occupant. In addition, this model is a complete feedforward model, which cannot flexibly adjust according to environmental changes and events. The occupant’s adaptive behavior is stochastic in this model. Therefore, there is a big gap between the calculated results and the actual situation. (2) Stochastic mode Developed from a fixed model, the stochastic mode has a better capacity for occupant behavior description. Prediction model with probability and statistics to predict

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Fig. 1 The methods of occupant behavior model

the action. In the current research, a stochastic model is primarily utilized as the primary methodology for characterizing occupant behavior. Due to its inherently unpredictable nature, the stochastic process of occupant behavior aligns more closely with the actual scenario. However, this type of model exhibits certain limitations, and it fails to address the issue of complexity in occupant behavior. The linear response model of logistic regression has a non-normal distribution which makes it difficult to predict the upper and lower limits of observations. At present, many researchers believe that the logistic regression model is only suitable for estimating the probability of adaptive occupant behavior relative to specific predictive variables [8]. In terms of the Markov stochastic model, it not only considers the influence of environmental parameters but also the influence between the two states before and after [31]. It assumes that the current state has nothing to do with the past state and that the next state only depends on the current state. It has advantages in the multi-state transition description [33]. The agent-based model has also been gradually applied in recent years. The main assumption is that an agent is a system that can constantly perceive “the environment” and interact with the environment spontaneously [19]. The model can be used to explore the interaction between people and people and the environment by understanding it as an agent with cognitive ability and the ability to make choices and commitments. Machine learning is not a specific algorithm per se, but rather a method of extracting patterns of occupancy or behavior from occupancy-related data. Cluster analysis in machine learning uses an unsupervised process to group data into categories based on some intrinsic similarity or distance measure [1]. K-means clustering is such an unsupervised process. At the same time, the occupant behavior is caused by the change of environmental factors. However, the function value of this kind of model is always more than zero, so the choice of the function form is also questionable. In addition, data mining can be realized with different algorithms that have very different concepts of what constitutes a cluster and how to find a cluster efficiently. This method has great advantages of inaccuracy, but it cannot form a mathematical description when used and depends on fixed building structure and environment. When applied to other buildings, it needs to be re-modeled due to its poor portability.

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Table 1 Characteristics and limitations of the occupant behavior models Mode

Characteristics

Limitations

Fixed

Simple and direct to use, widely used in engineering

Complete feedforward deterministic model, significant difference in the results and reality

Stochastic The method of statistics, probability and machine learning is applied to simulate the stochastic process, and the fitting is more accurate and effective Hybrid

The model is difficult to consider the complex situation of multiple factors, and the independent variable factors and function form adopted by the black-box type cannot form a mathematical description, so the methods cannot be duplicated in another place

Complex coupling of multiple There is no mature model and analysis software factors can be considered currently

(3) Hybrid mode The hybrid mode does not provide a more accurate description of actions on the model but can increase model accuracy by considering multiple variables in tandem. Research in occupants’ multi-factor control methods in the building can better describe the occupant control behavior. Indeed, some models have added an automatic control system that assumes to better estimate construction load and energy consumption through the interaction between different factors control behavior, and linkage. However, there is a lack of this integrated software to meet this requirement. Although modeling methods in current research still have some limitations, they have gradually improved in their capacity to more accurately and effectively describe occupant behaviors within buildings. Compared to deterministic models, stochastic models can better describe human behavior. Additionally, current research trends have shifted towards multi-faceted interactions and increased complexity. The characteristics and limitations of these three types of models are summarized in Table 1.

3 Occupant Behavior Model of Shading Behavior in an Office Building 3.1 Features of Office Buildings At present, the problem of energy conservation in this area has attracted extensive attention from the academic circle, which is one of the key areas of energy conservation and emission reduction in China. Office building is one of the main types of public buildings, and the growth rate of construction is fast. With the rapid development of China’s economy, the activity of small and medium-sized private enterprises has brought about the rapid expansion of the scale of the building. The

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office building is the main type of building in society. Due to the need to accommodate a diverse range of complex and dynamic functions, office buildings have been designed in a multitude of architectural styles. This has led to the emergence of distinct forms such as linear, massive, and hollow squares. Because of commonness and long energy consumption time, their various energy consumption indicators are much more than residential buildings with great dependence on energy. Due to these differences, occupant shading behavior in a building has a great impact on building energy consumption, different energy-saving technologies need to be adapted for different occupant behavior and lifestyle. Therefore, studying the occupant shading behavior in the office building has great significance to the realization of the overall goal of energy conservation.

3.2 Fixed Model in Shading Behavior The fixed model has been used widely not only in the engineering field but also in the research field of shading regulation. Lee and Selkowitz [15] used DOE-2 software to simulate and analyze the influence of active shading on building energy consumption. They assumed that indoor workers would automatically turn off active shading when the direct solar radiation exceeded 94.5 W/m2 , and the research results showed that the energy-saving effect could reach about 30%. Reinhart [23] conducted a similar study, which considered the boundary condition of the closing of active shading as the direct solar radiation exceeding 50 W/m2 . From the perspective of further reducing energy consumption and glare, Tzempelikos and Athienitis [28] reduced the direct solar radiation to 20 W/m2 , which was used as the boundary condition for active shading adjustment. In addition to solar radiation, some researchers regard temperature as the main cause of adjusting active shading. Georg et al. [6] studied the impact of the combination of active shading and electrochromic glass windows on building energy consumption. Their study took a temperature over 25 °C as the condition for the closure of active shading to reduce indoor overheating in summer. However, the study of Littlefair et al. [16] suggested that if the outdoor temperature is continuously less than 16 °C within 24 h, the active shading should be turned on to increase the indoor solar radiation heat. Olbina and Hu [22] also used indoor illumination as the basis for the shading adjustment of movable louvers and adjusted the shading angle of movable louvers when the illumination exceeded 2000 lx in order to control the appropriate indoor photo-thermal environment. In China, there are similar activities in behavior research reports, such as the study conducted by Huang and Hu [10]. They utilized the Radiance and Daysim design software to analyze the energy-saving effects of shading on residential buildings, considering solar radiation in various cities. They found the area of the window has strong effect on the shading performance. The research results of Sha et al. [25] show that the contribution of external blinds is about 20%. They studied the energy-saving effect of using different movable shading for typical office buildings in hot summer and cold winter areas, in which the position and angle of shading are automatically controlled

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according to the standard of glare control Daylight Glare Index (DGI) index less than 22 in summer for both shutter and rolling shutter shading [25]. All the above studies assume that the movable shading is automatically adjusted by some ideal fixed control mode but ignore the random uncertainty in the shading behavior of indoor personnel, which is not in line with the actual situation. However, such a simple setting of the fixed model makes it widely used in engineering and early research fields, such a description method is an average fixed model, which cannot represent the randomness of building users. Moreover, this model cannot be flexibly adjusted according to environmental changes and events, so there is a big gap between the calculation results of the model and the actual situation.

3.3 Stochastic Model in Shading Behavior There have been many studies on stochastic models of shading behavior. Because shading of adjustment behavior is influenced by various subjective and objective factors (such as solar radiation, the intensity of illumination) [9], glare [29], temperature, hot perception [6], many factors are difficult to measure or even not available. Hence the manual control adjustment of the activities of the sun is a very complicated nonlinear process and could not be determined by using the equation or functions to predict the adjustment of the shading. In addition, relevant international studies have proved that manual control of active shading adjustment has a great random uncertainty [9]. For this reason, researchers began to study the random adjustment characteristics of movable shading [14, 26]. By observing the relationship between environmental factors (such as solar radiation, illumination, and temperature) and the shading adjustment states, more researchers use the nonlinear regression method to analyze the main factors influencing the adjustment of movable shading behavior and established a nonlinear probability model. For example, for the first time, Nicol [20] takes shading behavior as a stochastic process by observing the sun. Outdoor temperature is considered the major factor for shading adjustment and the binary classification Logit regression method is applied to construct the shading behavior model. The sun shading can be divided into full open and fully closed two states and is analyzed the relation between the shading state along with the change in outdoor temperature. Haldi, Inkarojrit, and others respectively studied movable shading for one office building activity [20]. Through the single variable regression and multivariate regression, the binary classification logit analysis method was selected to study the relationship between solar radiation, the intensity of illumination, the temperature sort of environmental factors, and movable shading adjustment. Their research found that there is a strong correlation between the intensity of illumination, solar radiation, and shading adjustment, and they establish the corresponding binary classification Logit model [9, 11]. Chuang [3] also proposed a stochastic model for shading behavior and used a conditional feedback probability model to analyze the possibility of shading on or off depending on environmental factors.

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However, due to the binary classification logit analysis method used in these studies of stochastic shading behavior, the regression results only have two shading states, which are fully open and fully closed. Neglecting the partially open shading state fails to align with the practical reality of shading adjustments. For this reason, Yao [31] proposed a Markov chain-based stochastic shading mathematical model for movable shading based on an office building. This model can predict 5 different shading states of rolling blinds (including partial shading) and has good resolution and prediction ability to shade adjustment states. It can be seen from the above exploration and research that the accuracy of the current stochastic model has been greatly improved [30]. Since the shading behavior is based on the basic fact that people are indoors. Shading state adjustment cannot occur if no one is indoors [24]. Professionals in office buildings frequently go outside for business purposes. Studies show that only 70% of the time is spent indoors during the working period [18]. Approximately 2.5 h per day are dedicated by individuals to engaging in businessrelated activities outside their workplaces. In addition, although people’s commuting to and from work is regular, the time of each commuting to and from work and the events such as going out to do errands are not fixed and have great randomness [3]. However, Yao’s study did not consider the changing characteristics of indoor occupancy, so it was unable to accurately analyze the effectiveness of the shading adjustment. Indeed, this omission meant it could not elucidate the actual causes behind such phenomena as the delay in shading adjustment. Hence the occupancy in the room for the stochastic model of shading is necessary.

3.4 Hybrid Model for Shading Behavior Ding et al. [5] established a linkage adjustment model of shading and lighting in an office building in Wuhan based on desktop illumination. The shading behavior method would be based on the premise of satisfying indoor visual comfort. However, the site-measured data is only three months, the results are not of a great reference value. A study by Kunwar et al. [13] coupled the optimization performance of the shading model with Daysim, a professional lighting analysis software that improves the accuracy of the light environment in the shading model. But the need to switch back and forth between different software does not realize coupling analysis [13]. Burak in Canada developed a control algorithm based on the use of adaptive lighting and blinds, which substantially reduce the lighting loads in office buildings without affecting occupant comfort [7]. Therefore, future research work should have largescale implementation campaigns to better understand the role of adaptive controls on load diversity as these two studies are only based on a small number of office room data sets.

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4 Evaluation Method of Energy Saving in Shading Behavior At present, the existing building energy simulation software (such as DOE-2, ESP-R, DeST, PKPM, etc.) cannot analyze the stochastic adjustment characteristics of active shading due to the limitation of function. Most of them are considered according to environmental boundary conditions or some fixed adjustment mode. Hence, it is difficult to directly create the stochastic behavior model of active shading in building energy consumption simulation software. In addition, a lot of simplification and assumptions must be made so that the stochastic adjustment model of active shading becomes a deterministic model (that is, shading adjusts according to certain boundary conditions). According to the literature review of existing active shading behavior studies by O’Brien et al. [21], no study has coupled these stochastic models with energy consumption software and analyzed the impact of active stochastic shading adjustment on building energy consumption. Chen et al. [2] realized the coupling analysis between the shading behavior model and energy simulation in the office building. They developed the Lawrence Berkeley laboratory building performance integration analysis platform Building Controls Virtual Test Bed (BCVTB) coupling stochastic shading behavior model and the Energyplus in the studying of comparison energy performance between simulation and reality. However, this study was only based on five office rooms. Burak developed a self-control system based on the control algorithm which developed the use of adaptive lighting and blinds, which can substantially reduce the lighting loads in office buildings [7]. However, the pattern provided by this method is difficult to link to the energy simulation program [12]. In conclusion, current research on annual energy consumption and load characteristics of active shading is not in-depth enough. In addition, there is a lack of analysis on the uncertainty of the calculation of maximum cooling and heat load and energy consumption of rooms with different orientations. Therefore, it is urgent to study the energy-saving equivalent evaluation method of stochastic shading adjustment being suitable for PKPM and other engineering energy-saving software.

5 Discussion From the above reviews, there has been much research in the field of occupant behavior of shading behavior in office buildings. However, few studies have developed a quantitative approach to optimizing the shading behavior model and its evaluation method of energy consumption performance in tandem. Based on this, there are three aspects for future research: 1. Analyzing the convenient levels of shading behavior and the diversity of occupants would greatly improve the accuracy of the shading occupant behavior model.

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2. The realization of the shading behavior model and energy consumption coupling analysis is of great significance to an evaluation of the energy consumption impact of shading behavior. 3. Automatic control system would be a necessary part of the occupant behavior model to have customized needs in thermal and visual comfort. Combined with the shading behavior model and evaluation method, it provides effective guidance for the establishment of intelligent automatic control systems of office buildings.

6 Conclusions This article has reviewed the literature related to occupant behavior in buildings, the shading behavior model, and its evaluation method. The conclusions list under below: ● There is of great significance shading adjustment to building energy-saving. Meanwhile, the evaluation method of shading adjustment on building energy consumption needs to be improved. ● Compared to fixed models, the stochastic model has a better predictive capacity for occupant behavior. Furthermore, it has the potential to explore occupant diversity and the convenience of shading. ● The coupling analysis of the shading behavior model and energy consumption simulation would be beneficial to the shading effect on buildings. ● The intelligent shading control system is an effective way to realize better building energy-saving.

References 1. Aerts D, Minnen J, Glorieux I et al (2014) A method for the identification and modelling of realistic domestic occupancy sequences for building energy demand simulations and peer comparison. Build Environ 75:67–78 2. Chen G, Yao J, Zheng R (2021) Energy related performance of manual shading devices in private offices: an occupant behavior-based comparative study using modeling approaches. Case Stud Therm Eng 27:101336 3. Chuang W (2014) Simulation research on occupant energy-related behaviors in building. Tsinghua University Press, Beijing 4. Deng X, Xie J, Teng F (2021) What is carbon neutrality? Clim Chang Res 17(1):107–113 5. Ding Y, Ma X, Wei S, Chen W (2020) A prediction model coupling occupant lighting and shading behaviors in private offices. Energy Build 216:109939 6. Georg A, Graf W, Schweiger D, Wittwer V, Nitz P, Wilson HR (1998) Switchable glazing with a large dynamic range in total solar energy transmittance (TSET). Sol Energy 62:215–228 7. Gunay HB, O’Brien W, Beausoleil-Morrison I, Gilani S (2017) Development and implementation of an adaptive lighting and blinds control algorithm. Build Environ 113:185–199

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8. Haldi F, Robinson D (2010) On the unification of thermal perception and adaptive actions. Build Environ 45:2440–2457 9. Haldi F, Robinson D (2011) The impact of occupants’ behaviour on building energy demand. J Build Perform Simul 4:323–338 10. Huang L, Wu J (2014) Effects of the splayed window type on daylighting and solar shading. Build Environ 81:436–447 11. Inkarojrit V (2005) Balancing comfort: occupants’ control of window blinds in private offices 12. Jia M, Srinivasan RS, Raheem AA (2017) From occupancy to occupant behavior: an analytical survey of data acquisition technologies, modeling methodologies and simulation coupling mechanisms for building energy efficiency. Renew Sustain Energy Rev 68:525–540 13. Kunwar N, Cetin KS, Passe U (2021) Calibration of energy simulation using optimization for buildings with dynamic shading systems. Energy Build 236:110787 14. Laaroussi Y, Bahrar M, Elmankibi M, Draoui A, Si-Larbi A (2020) Occupant presence and behavior: a major issue for building energy performance simulation and assessment. Sustain Cities Soc 63:102420 15. Lee ES, Selkowitz SE (2018) The design and evaluation of integrated envelope and lighting control strategies for commercial buildings 16. Littlefair P, Ortiz J, Bhaumik CD (2010) A simulation of solar shading control on UK office energy use. Build Res Inf Int J Res Dev Demonstr 38:638–646 17. Lu Xumin SJ, Zhaopeng H, Weixiang Z, Song W (2021) Differentiated comparison based on building energy conservation strategies: a case study of Germany and Japan. Energy Energy Conserv 11:52–56 18. Mahdavi A, Mohammadi A, Kabir E, Lambeva L (2008) Occupants’ operation of lighting and shading systems in office buildings. J Build Perform Simul 1:57–65 19. Malik J, Azar E, Mahdavi A, Hong T (2022) A level-of-details framework for representing occupant behavior in agent-based models. Autom Constr 139:104290 20. Nicol JF (2001) Characterising occupant behaviour in buildings: towards a stochastic model of occupant use of windows, lights, blinds, heaters and fans. In: Proceedings of the seventh international IBPSA conference, Rio, pp 1073–1078 21. O’Brien W, Kapsis K, Athienitis AK (2013) Manually-operated window shade patterns in office buildings: a critical review. Build Environ 60:319–338 22. Olbina S, Hu J (2012) Daylighting and thermal performance of automated split-controlled blinds. Build Environ 56:127–138 23. Reinhart CF (2004) Lightswitch-2002: a model for manual and automated control of electric lighting and blinds. Sol Energy 77:15–28 24. Schweiker M, Hawigihorst M, Wagner A (2016) The influence of personality traits on occupant behavioural patterns. Energy Build 131:63–75 25. Sha HJ, Xu P, Tang WW (2012) Optimal design of shading and daylighting for an office building in Shanghai. Build Energy Effic (9):33–36 26. Song K, Kwon N, Anderson K, Park M, Lee H-S, Lee S (2017) Predicting hourly energy consumption in buildings using occupancy-related characteristics of end-user groups. Energy Build 156:121–133 27. Too J, Ejohwomu OA, Hui FK, Duffield C, Bukoye OT, Edwards DJ (2022) Framework for standardising carbon neutrality in building projects. J Clean Prod 133858 28. Tzempelikos A, Athienitis AK (2007) The impact of shading design and control on building cooling and lighting demand. Sol Energy 81:369–382 29. Wienold J, Christoffersen J (2006) Evaluation methods and development of a new glare prediction model for daylight environments with the use of CCD cameras. Energy Build 38:743–757 30. Wolf S, Cal`ı D, Krogstie J, Madsen H (2019) Carbon dioxide-based occupancy estimation using stochastic differential equations. Appl Energy 236:32-41 31. Yao J (2014) Determining the energy performance of manually controlled solar shades: a stochastic model based co-simulation analysis. Appl Energy 127:64–80

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Case Studies in Sustainability

Real-Time Chiller Optimization in an Industrial Plant with Data-Driven Load Forecast Approach: A Case Study Siliang Lu

Abstract Currently, digitalization is bringing about a paradigm shift within the current mitigation of climate change. As more and more industrial giants acknowledge the need to overcome global warming, many are making efforts to enhance automation and optimization to increase efficiency, reduce fuel costs and, ultimately, to realize carbon neutrality. Owing to hybrid modeling techniques and digital building energy systems, it is now easier to monitor, analyze, and optimize the flow of energy consumption within manufacturing operations, thus delivering an updated picture of where savings come from. Therefore, this case study describes the optimization of data-driven chiller performance with real-time cooling load forecast and control and domain knowledge in an industrial plant. Compared to conventional chiller operations in the plant where facility engineers manually optimize the system control by adjusting the chilled water supply temperature and pressure differential set-points with their domain knowledge only, the proposed hybrid modeling system realizes dynamic control and can guarantee a continuous 24-h manufacturing process in the plant. As a result, an average of 10% COP improvements of the chiller system was achieved after one-year operation at the end of 2021. Moreover, since such a system has high scaling potential, a cloud-based solution will be deployed across all manufacturing plants in future. Keywords Time-series load forecast · Model selection · Fuzzy control · Chiller system optimization · Hybrid modeling

1 Introduction Inspired by the emerging concept of environmental, social, and corporate governance (ESG), more and more industrial giants have taken the social responsibility to lead their companies to be carbon neutral. To realize carbon neutrality, such S. Lu (B) Bosch Center for Artificial Intelligence, Shanghai, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 T. Zhou et al. (eds.), Smart Buildings and Technologies for Sustainable Cities in China, Urban Sustainability, https://doi.org/10.1007/978-981-99-6391-1_10

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industrial giants have been making considerable efforts to enhance automation and digitalization to improve energy efficiency, especially in manufacturing plants. As one of the most important components in the energy system of a plant, the chiller system plays a key role in creating a suitable indoor environment, particularly a thermal environment, and indoor air quality in industrial buildings. However, existing operation strategies such as rule-based control cannot guarantee optimized performances. Hence, there is a growing interest in industry to determine the optimized performance of chiller systems in industrial buildings. Several research studies on chiller optimization have been published in which global optimization frameworks based on data-driven chiller plant power forecast models have been developed. Moreover, an empirical evaluation with forecast-based chiller optimization by Wang et al. [5] on a real-world chiller plant has achieved energy savings by 7%. In addition, Dung Vu et al. [3] proposed a cooling-load forecast predictive control with a building energy simulation and global optimization. Moreover, Chen et al. [2] investigated multi-chiller optimization with four different control strategies with a building energy simulation. In addition, Shan et al. [4] proposed an effective and robust strategy for centrifugal chiller plants with indicators such as inlet guided vane and chiller current. Even if global optimization with algorithms such as constrained optimization can achieve a global optimum in theory, the optimal results from data-driven forecast models are highly dependent on the historical data and chiller configurations used in the past. Hence, the search space, or the domain of the function to be optimized, is limited. Moreover, since forecast models with historical data may develop biases after a certain period of operation, it is risky to use such forecast models without retraining. Finally, even if building energy simulations can cover the complete search space, it is difficult to calibrate a building simulation with real-world industrial plants. Compared to existing projects and papers, instead of global optimization based on forecast models with machine learning algorithms, this use case aims to develop a real-time fuzzy control system based on load forecast models with machine learning algorithms. Moreover, since the data-driven model forecast performances are likely to decrease over a certain period of operation, a user-friendly retraining module with the algorithms mentioned above is also developed. This is activated by a forecast evaluation metric called mean absolute percentage error (MAPE) when MAPE is greater than 5.

2 Methodology 2.1 Data Collection The use case was conducted in an industrial plant located in Qingdao, China. The plant area is 18,865 m2 with five buildings, as shown in Fig. 1. Due to the high annual mass production demand, the plant needs cooling all year round to meet the

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temperature targets for different machines. Therefore, an energy station was built for cooling the manufacturing areas and office areas in the plant. Figure 2 shows the basic system diagram of the cooling system in the plant. As presented in the figure, the HVAC mainly consists of a chilled water system and a cooling water system. On one hand, the chilled water system supplies the chilled water from the chiller to the interior of the building, for cooling the manufacturing areas with a direct chilled water system as well as cooling the office areas with an air handling unit (AHU) or fan-coil unit (FCU). On the other hand, the cooling water system supplies cooling water from the chiller to the cooling tower to emit excessive heat to the exterior of the building. Furthermore, owing to the building management system, the plant has collected a massive amount of data from a database to monitor the operation of the cooling system. Currently, there are over a thousand data points from various types of sensors and meters. However, with the existing rule-based cooling system control, it seems this considerable amount of data has not been fully utilized. Therefore, this case study has a high potential to optimize the chiller performance with a real-time cooling load forecast and control based on the database. Table 1 shows the data description for the proposed optimization solution. The sampling frequency of raw data is 1/min. However, taking the thermodynamics of the chiller system into account, the control frequency is hourly. Therefore, the raw data is resampled into hourly data. Moreover, the chilled water supply and return temperature as well as the chilled water flow rate are used for the calculation of the actual cooling load for the forecast model.

Fig. 1 Plant overview

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Fig. 2 Basic diagram of the HVAC system in the plant

Table 1 The features extracted from the database

Feature name

Unit

Outdoor ambient temperature

[°C]

Outdoor relative humidity

[%]

Chilled water supply temperature

[°C]

Chilled water return temperature

[°C]

Chilled water flow rate

[m3 /h]

2.2 Development of Hourly Cooling Load Forecast Model Generally, cooling load forecasts are unlikely to be calculated explicitly since they are not only affected by excessive heat generated by various types of working machines but also affected by other factors such as the outdoor environment, production schedules, and occupant behaviors in the plant. Therefore, instead of physical models, the cooling load forecast is developed with ensembled data-driven models. In terms of the development of the cooling load forecast model, at least one month of historical data is used for training and evaluation. After pre-processing such as missing data imputation and outlier removal, the input features of the model include outside temperature and actual cooling load of the last hour and the output is the predicted cooling load of the next hour. Moreover, to automatically update the model after a certain period of time, an automatic retraining module is developed, and the best model is selected with a model selection algorithm called time-series cross validation when the forecast performance has become unsatisfactory. As mentioned before, the invention uses various linear and

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Fig. 3 Pipeline of cooling load forecast model

nonlinear regression algorithms including Support vector regression, Random forecast regression, Decision tree regression, Ridge regression, Gaussian process regression, Linear regression, Adaboost regression, and Gradient boosting regression. In addition, MAPE is used to evaluate the performances of forecast models where the better the forecast performance, the lower the MAPE. Currently, the threshold of MAPE for retraining is 5. Figure 3 shows the pipeline of the development of cooling load forecast models.

2.3 Real-Time Control System The coefficient of performance (COP) of the chiller system is the ratio between the cooling load and electrical power. In terms of the real-time control system, the first principle of the chiller system is that the COP is positively correlated with the chilled water supply temperature set point and negatively correlated with the chilled water pressure differential set point. Since the higher the COP is, the more energy efficient the chiller system will be, the control system is developed with a fuzzy control algorithm based on both the first principle and the data-driven cooling load forecast model. Moreover, compared to chiller power, the power of chilled water pumps can be neglected in the plant; thus, only the chilled water supply temperature is optimized. In detail, for each hour, the chilled water supply temperature set-point is adjusted based on the difference between the predicted cooling load and the actual cooling load of the next hour. If the predicted cooling load is larger than the actual cooling load of the past hour ti , the set-point for the next hour ti+1 will be increased to improve energy efficiency. Otherwise, it will be decreased to satisfy cooling demand. Moreover, the control system keeps manual control in case of any unpredictable situations such as a plant shutdown plan. Figure 4 shows the real-time control diagram in the plant.

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Fig. 4 Real-time control diagram with cooling load forecast model

3 Result Analysis In conventional chiller operations, facility engineers manually optimize the system control by adjusting chilled water supply temperature and pressure differential setpoints with their domain knowledge only. Comparatively, the current system realizes dynamic control and can guarantee continuous 24-h manufacturing processes in the plant with about 10% COP improvements on average. Figure 5 shows the COP benchmark between 2020 and 2021 from January to May. As shown in the figure, most of the months in 2021 have COP improvements except April, which can be explained by cooling capacity insufficiency. In other words, the chiller system was operated in full load mode during most of the days in April this year. Due to the chiller’s specific characteristics, the COP of the chiller in full load mode is lower than that in part load mode with dynamic chilled water temperature set point control. In addition, Fig. 6 shows the violin plot of the annual COP distribution between 2020 and 2021 where the average COP has increased from 6 to 6.6 with the proposed solution with 10% improvements.

Fig. 5 COP benchmark between 2020 and 2021

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Fig. 6 Annual COP benchmark between 2020 and 2021

Moreover, Fig. 7 shows the energy consumption benchmark in January between 2020 and 2021. As a result, a 10% energy reduction was achieved with the proposed optimization solution among similar weather conditions and production conditions. Moreover, in terms of the performances of the cooling load forecast model, Fig. 8 shows cooling load forecast performances between the 8th and 22nd of January 2021. As shown in the figure, the MAPE of the forecast model is 4.1%, which is lower than the retraining threshold. As a result, it was not necessary to retrain the forecast model during that time.

Fig. 7 Energy benchmark in January between baseline and proposed solutions

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Fig. 8 Performances of cooling load forecast model

4 Discussion In this use case, even if the real-time optimization of the chiller system has achieved savings, there are still some future research directions to consider. Firstly, the current solution has applied a hybrid modeling design pattern where the data-driven forecast model is used for control with domain knowledge in the HVAC industry, as shown in Fig. 9. In the future, more hybrid modeling design patterns could be applied in the proposed solution. Secondly, as discussed in the results analysis section, the improvements are also affected by the cooling load capacity of the specific chillers, which indicates the importance of chiller capacity selection before deployment. Moreover, since central chiller plants with multiple chillers are typically used for space cooling in large buildings, which account for about half of the HVAC energy consumption during the cooling period [1, 6], multi-chiller optimization with a data-driven cooling load forecast is worth investigating from an industrial point of view [2]. Finally, with the development of a cloud platform, the proposed solution in the study has high scaling potential across multiple plants since it can be generalized and easily adapted to similar manufacturing plants in the future.

5 Conclusions To achieve the goal of carbon neutrality in the industry, many industrial giants have made significant efforts to improve energy efficiency in manufacturing plants. This chapter’s use case in a real manufacturing plant describes a data-driven chiller optimization approach with real-time cooling load forecast and control, which is suitable

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Fig. 9 Hybrid modelling design pattern for the solution

for deployment in manufacturing plants to optimize chiller performances. During its operation, the chiller system has achieved an average of 10% COP improvements. Moreover, it has high scaling potential with cloud solutions for a data-driven chiller control system across all manufacturing plants.

References 1. Cao Y, Wang Q, Wang Z et al (2020) A new optimized configuration for capacity and operation improvement of CCHP system based on developed owl search algorithm. Energy Rep 6:315–324. https://doi.org/10.1016/j.egyr.2020.01.010 2. Chen Y, Yang C, Pan X, Yan D (2020) Design and operation optimization of multi-chiller plants based on energy performance simulation. Energy and Buildings 222:110100. https://doi.org/10. 1016/j.enbuild.2020.110100 3. Dung Vu H, Chai KS, Keating B et al (2017) Data driven chiller plant energy optimization with domain knowledge. In: Proceedings of the 2017 ACM on conference on information and knowledge management, pp 1309–1317 4. Shan K, Wang S, Gao D, Xiao F (2016) Development and validation of an effective and robust chiller sequence control strategy using data-driven models. Autom Constr 65:78–85. https://doi. org/10.1016/j.autcon.2016.01.005 5. Wang L, Lee EWM, Yuen RKK, Feng W (2019) Cooling load forecasting-based predictive optimisation for chiller plants. Energy Build 198:261–274. https://doi.org/10.1016/j.enbuild. 2019.06.016 6. Wang Y, Jin X, Du Z, Zhu X (2018) Evaluation of operation performance of a multi-chiller system using a data-based chiller model. Energy Build 172:1–9. https://doi.org/10.1016/j.enb uild.2018.04.046

Sustainability: Design Strategies and Applications in a Shanghai Commercial Complex Junqiang Wang, Yuran Kong, and Yang Jiao

Abstract With the ongoing development of the economy and the improvement of living standards, commercial complexes have become increasingly favored by real estate over the past few years. Their innovative designs, intricate energy usage, and environmental impact on their surroundings have made the design process of sustainable and environmental-friendly commercial complexes a popular topic. This chapter presents a commercial complex in the Qiantan area of Shanghai, which is planned to be the new financial district. The project spans 215,609.5 m2 and consists of three buildings. During the design period, the team employed advanced design concepts and sustainable strategies to help the project attain the highest level in the Chinese Green Building assessment system. This paper delves into the energy-saving features, comfortable indoor environment, water conservation efforts, and contributions to carbon neutrality that were achieved throughout the project. The insights from this paper aim to serve as a reference guide for designing high-performance commercial complexes that contribute to environmental sustainability and carbon neutrality.

1 Introduction In the context of carbon peaks and carbon neutrality, resource conservation has become increasingly critical. Given that a building’s energy consumption contributes to a substantial portion of urban carbon emissions, it should be the first component to optimize. Therefore, high performance both during construction and operation, as well as environmental impact, are gaining emphasis. At the same time, with economic growth, a building should not only meet basic energy-saving needs but also ensure thermal comfort for users. From a citywide perspective, a commercial complex presents complex scenarios. This includes the mechanical, electrical, and plumbing (MEP) systems during design and operation, personnel flow, comfort requirements, and more, all of which should J. Wang · Y. Kong · Y. Jiao (B) TIANHUA Architecture Planning & Engineering Ltd., Shanghai, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 T. Zhou et al. (eds.), Smart Buildings and Technologies for Sustainable Cities in China, Urban Sustainability, https://doi.org/10.1007/978-981-99-6391-1_11

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ideally be considered and planned during the design phase. Therefore, this chapter uses a project in Shanghai to carry out an integrated analysis of how to implement sustainable technologies for commercial complexes in the design phase. The project is located in the southern riverside area of Shanghai’s Huangpu River, which has complete service facilities and municipal infrastructure. The project comprises three buildings with four main functionalities. The first building is a highrise office tower with commercial spaces from 1 to 3F, the second one is a cultural performance center, and the third is a hotel. The total floor area of the project is 215,609.5 m2 , with an aboveground area of 140,286.5 m2 , and an underground area of 75,323 m2 . The office tower comprises 3 floors of commercial space and 28 floors of office space. The cultural performance center includes a large multifunctional performing arts hall and comprehensive cultural industry services. The hotel has 213 guest rooms and a large multifunctional hall. These buildings have been designed with a ribbon concept. As shown in Fig. 1, the concept of the sinuous form of the ribbon is an important connecting element of the project, which binds the buildings with a connecting corridor and enhances the connectivity between the project and the surrounding environment. The ribbon shape is continuously emphasized to promote harmony with the surrounding environment. This project uses a digital multimedia curved mesh system, which symbolizes cultural ties, to organically connect and interweave various formats. The huge curved digital multimedia screen brings people an intuitive visual feast of culture and art, as shown in Fig. 2.

Fig. 1 Project design concept

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Fig. 2 Project rendering

2 Sustainability Design Strategy—Site Design 2.1 Outdoor Environment There is an increasing awareness of the problem of undesirable pedestrian-level wind effects [2]. To establish a healthy and livable environment in urban areas, ventilation conditions are one of the essential features, and are significantly influenced by urban space forms. In urban design practice, morphological indicators are important points of assessment of the ventilation quality and for improving the ventilation conditions [15, 16]. Hong and Lin [11] indicate that the outdoor wind environment and pedestrianlevel thermal comfort can be significantly influenced by building layout patterns and tree arrangement. In addition, horizontal vortex air flows can be accelerated by long building façades which are parallel to the prevailing wind direction. Furthermore, a square central space configured with buildings could promote the prevailing wind against pedestrians. To provide a friendly walking environment, the project team carried out a ventilation simulation. Through reasonable building and green space layout, the wind speed during specific winter conditions was shown to be in the comfort zone, which is conducive to outdoor activities in winter and provides a comfortable outdoor environment for citizens; at the same time, the building spacing was reasonable, ensuring a view and improving natural ventilation during the transition seasons.

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2.2 Sponge City With the growth of human activities and footprint, the amount of wilderness, which has abundant vegetation and water bodies, has been gradually covered by buildings and roads. As a result, rainfall cannot soak through the impervious pavement in cites, instead flowing across and entering ditches or storm drains [7]. With accelerated urbanization and an increase in extreme storm events, it becomes ever more significant to respond to urban flood risks [4, 13]. Low Impact Developments (LIDs) and Sustainable Urban Drainage Systems (SUDS) are utilized in North America and Europe [8], which act as solutions to alleviate the negative impacts of urbanization, changing weather patterns, and aging infrastructures [12]. More examples in different countries include WaterSensitive Urban Design (WSUD) in Australia, Decentralized Urban Design (DUD) in Germany, Well-Balanced Hydrological System (WBHS) in Japan and Healthy Water-Cycle City (HWC2) in South Korea. LIDs are known as sponge cities in China, and act as solutions for urban waterlogging and storage, urban heat island (UHI) effects, and water blackening [25]. In China, the sponge city program has been implemented from 2016 onwards. The sponge infrastructures were introduced to detain runoff, control flooding, recharge groundwater, and reuse stormwater [17]. The index of a sponge city is the annual runoff control rate, which refers to the proportion of the accumulated annual controlled rainwater in the total annual rainfall through natural and artificial reinforcement of infiltration, storage retention, storage regulation, and rainwater collection and reuse. The project that is the focus of this chapter was designed with sponge infrastructures, which can reach a 74.93% runoff control rate, as shown in Fig. 3. The proportion of permeable pavement area reaches 50.1% and its location is mainly used in non-motor vehicle parking spaces, pedestrian and public areas, etc. In addition, recessed green spaces, green roofs, and rainwater recycling systems have also been equipped on the site.

2.3 Lighting Pollution For a commercial complex, atmospheric night lighting is an important means of showing architectural charm and coordinating with the decoration of urban areas to create an atmosphere reminiscent of cultural landmarks at night. In the design of floodlighting, through analyzing the traffic and pedestrian fields of view, both the aesthetic effect and light pollution in residential areas have been fully considered. The vertical surface illuminance before the lights-off period is about 13.81 lx, and the vertical surface illuminance during the lights-off period is less than 5 lx. The luminous intensity limit value of the building facade is 21,000 cd; it does not cause glare to pedestrians and non-motorized vehicles, and does not produce upward light.

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Fig. 3 Major sponge infrastructures

On the premise of ensuring the safety of civil activities, the production of light pollution is controlled to the greatest extent.

2.4 Urban Heat Island Population and urbanization will continue to increase in the next several decades. The growth of cities aggravates local changes in land cover, climate, and hydrological cycles [10], which results in the UHI effect. Urbanization alters the surface energy balance of the urban surface morphology [19], which absorbs and reserves radiant heat, resulting in an increase in ambient and surface temperatures and sometimes producing extremely high temperatures [1, 20, 24]. Alleviating the UHI effect is one of the most important means of meeting energy conservation and pollution reduction targets. Rational architectural layout and landscape design are significant measures to achieve building energy efficiency and sustainability. To reduce the UHI effect, the project has set the ground landscape area as 2671 m2 and the green roof area as 2618.13 m2 , respectively. In addition, to establish pleasant pedestrian areas, building structure shadings and plant shadings have been utilized to provide shade over the paving area, which could also contribute to reducing the UHI effect by decreasing heat absorption and retention.

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3 Sustainability Design Strategy—Resource Saving Sustainable design should also pay attention to saving non-renewable resources, particularly electricity, gas, and water at the building level. Shanghai is one of the most developed cities in China with a booming economy and growing population, which causes greater demand for resource consumption. Shanghai, situated on the Huangpu River and the Yangtze River Estuary, still suffers acutely from a lack of available clean water, mainly due to poor water quality [22]. In addition, although Shanghai Electric Power continues to promote wind and photovoltaic power generation projects due to concerns for the environment, by 2020, the proportion of thermal power generation in Shanghai still accounted for more than 50% [18]. To save such resources, the project has concentrated on improving water and energy efficiency which can reduce resource consumption during the operation period.

3.1 Water The project under review consists of an office, a hotel, commercial spaces, and a theater. The indoor water consumption of the project includes the usage of the toilet, restroom, cooking, and other areas, with the toilet constituting the majority of water use [15, 16]. Due to the operational nature of the complex, efforts to reduce indoor water consumption have focused predominantly on the efficiency and performance of water equipment. Firstly, in consideration of characteristic water usage conditions, the project has opted to use efficient plumbing fittings and fixtures that meet the highest level of water efficiency regulations for each equipment type in China, as shown in Table 1. Meanwhile, to ensure the performance of these fixtures, the project has controlled the pressure of each water point not to exceed 0.2 MPa. The water supply system is segregated by floor height; floors between B3 and the first floor are supplied by the municipal drainage system (0.16 MPa), and all floors on and above the second floor are supplied by the pressurized water system. Through rational zoning, the pressure Table 1 Project water consumption of fixtures and fittings Fixture or fitting Design case Highest Chinese standard (regulation code) Baseline in LEED Toilet

3.3 lpf

4 lpf (GB 25502)

6 lpf

Faucet

1.58 lpm

6 lpm (GB 25501)

1.9 lpm

Urinal

0.3 lpm

2 lpf (GB 28377)

3.8 lpf

Showerhead

4.8 lpm

4.8 lpm (GB 28378)

9.5 lpm

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at the lowest water usage point is capped at 0.45 MPa, and a valve ensures that branch pressure does not exceed 0.2 MPa. After implementing all water-saving measures for indoor water, it was found that the reduction in indoor water use reached 44%, compared to the baseline case shown in the Reference Guide for Leadership in Energy and Environmental Design (LEED). For outdoor water reduction, a water-saving irrigation system has been equipped with a soil moisture sensor to reduce unnecessary irrigation. Combined with the rainwater collection pool in the sponge city design, a rainwater reuse system has been set up. The recycled rainwater is used for greening irrigation, road washing, and landscape water replenishment. A water balance calculation based on Shanghai’s weather conditions showed that 5,421,800 L out of 6,801,110 L are supplied by the reused rainwater systems, representing an annual 79.7% usage. By using a highly efficient irrigation system and rainwater reuse system, the outdoor water reduction was able to reach 93.05%, compared to the baseline case shown in the Reference Guide for LEED.

3.2 Energy At the early design stage, it was decided that the project would feature a façade dominated by a glass curtain wall. A schematic energy simulation was carried out to figure out the optimized thermal performance of each kind of envelope, and to determine the appropriate window-to-wall ratio based on cooling and heating loads. Under the design parameter conditions, the heating and air-conditioning review showed a 15% lower impact than the baseline reported in the Design Standard for Energy Efficiency of Public Buildings (GB 50189). The project uses the district energy system as its cooling and heating source, with temperatures of 6 °C and 13°C, respectively, for supply and return chilled water, and 58 °C and 45 °C for supply and return hot water. After passing the heat exchanger, the chilled water supply of the building is designed to be 7 °C and the hot water supply is 55 °C. The HVAC system of the project differs depending on the space type. A Fan Coil Unit (FCU) with an Outdoor Air (OA) system was designed in the office outer zone, hotel guest rooms, and retail area. A variable air volume system was applied in most areas including the inner office zone, hotel and office lobby, and commercial common area. All of the air-side equipment uses highly efficient fans. All areas, except the office inner zone, use a four-pipe system, with the hot and cold water supply controlled by a load-regulated solenoid valve. Considering its load characteristic, which is primarily from equipment, the office’s inner zone uses a two-pipe water system that supplies chilled water throughout the year. Given the pressure differences caused by height, the water system of the office building was designed as lower zones and higher zones, equipped with variable speed pumps. When the measured pressure at the least favorable terminal deviates from the set value, the chilled water pump’s frequency is adjusted to maintain the

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HVAC terminals’ pressure. When the pump speed exceeds the limit, the system automatically adjusts the number of pumps. In addition, the project uses LED lighting, which has a higher light intensity while using less power. The lighting is controlled by a light sensor and automatic daylight controllers in sidelit areas, reducing the lighting level during sunny days. Furthermore, the lighting power density complies with the Standard for Lighting Design of Buildings (GB 50034), which prescribes 8W/m2 for offices and 9 W/m2 for retail areas. Thanks to the low lighting power and automatic control, the energy consumption for lighting is 20% lower than that of the baseline building in ASHRAE 90.1-2010. After all the optimizations made for the envelope and equipment, the project’s annual energy consumption was found to be 15.7% lower than the baseline building in the Chinese Design Standard for Energy Efficiency of Public Buildings (GB 50189).

3.3 Life Cycle Analysis of Carbon Dioxide Emissions Excessive emissions of greenhouse gases (GHGs), which can trap and redirect heat between the sun and the Earth, are the most important contribution to global warming [14]. In the building sector, GHGs mainly refer to carbon dioxide (CO2 ), and the construction and operation of buildings contribute to 30–40% of global CO2 emissions annually [14]. The life cycle of CO2 emissions can be demonstrated through the construction, operation, and demolition period (in which part of the building material will be recycled). In the construction phase, CO2 emissions are produced during the production and transportation of materials, and activities carried out during construction, which are all collectively termed ‘embodied GHG emissions’. At the operational stage, GHGs are emitted from daily energy consumption and absorbed by landscaping. All of the buildings in the project were designed as curtain walls with steel structures. The constituent glass, steel, and metal frames can be recycled during the phase of demolition, which would account for 16.34% of the weight of the building construction materials. Using the life cycle carbon emission calculation method provided by the Standard for Building Carbon Emission Calculation (GB 51366), it was deduced that the project emits 3611.63 tons of CO2 every year under a 50-year operation period. The results from the calculation are shown in Fig. 4. The carbon emissions from the building materials production process account for the largest proportion of the whole life cycle, reaching 66.04%; then, the daily energy consumption during the operation period contributes to 26.15% of the carbon emissions. However, 11.95% of carbon emissions are reduced by landscaping and material recycling, which gives a great contribution towards carbon reduction.

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Fig. 4 Carbon emission calculation of life cycle (tCO2 )

4 Sustainability Design Strategy—Indoor Environment Quality Sustainability for a building is not only devoted to reducing energy consumption but also to designing comfortable indoor environments for working and living. The indoor environment is of key importance for human health and well-being, not only due to the time spent indoors during our lifespan (approximately 90%), but also due to the combination of health and safety threats encountered on a daily basis [21]. A livable indoor environment is mainly combatted by having access to moderate daylight, appropriate air distribution, and sufficient fresh air.

4.1 Daylighting Natural light is one of the most important elements for living, as it not only brings daily energy, but also a richer visual experience under various lighting conditions compared to artificial light, which is beneficial to work efficiency [23]. The connection to the outdoors is also strengthened if more natural light comes into the space, which influences an individual’s satisfaction, attentiveness, and productivity as well. The project has aimed to improve access to daylight and viewing conditions from the scheme design by adjusting the orientation, floor plan, and building surface properties. The project uses a glass curtain wall with a Visible Transmittance (VT) larger than 45% to allow sufficient natural light into the room. Through the curtain wall, the park to the north and the river to the west become visible. In the office building, the core that serves for elevators and mechanical pipes is assigned in the middle of the floor plan, and all perimeter areas are designed as offices or meeting rooms. 90.08% of these areas meet the daylight factor requirement for each space type as given in the Standard for Daylighting Design of Buildings

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(GB 50033). Glare is minimized through the use of light-colored decorations and the building’s self-shading structures. The hotel guest rooms are designed with appropriate depth, and will be decorated with a veil curtain that ensures that natural light brightens most places with minimal glare. Calculations show that 76.38% of the guest room area meets the daylight factor requirement for each space type as per the Standard for Daylighting Design of Buildings (GB 50033).

4.2 Thermal Comfort Thermal comfort is a crucial indicator of indoor environment quality that impacts not only comfort but also productivity and even health. Satisfaction with the thermal environment is very important [6]. Thermal comfort in modern buildings is associated with the HVAC system design, including the type of HVAC terminal, space conditions of the conditioned room, supply air temperature, and speed. The project includes a five-floor-high theater intended to host grand events in the future. Its HVAC terminals include an underfloor air-conditioning system and a side air-conditioning system, equipped with a static pressure box before each outlet to ensure that audiences near this area experience a stable breeze. In order to determine optimized air distribution and thermal comfort, and to guarantee that the audience area has an appropriate wind speed and even temperature, a Computational Fluid Dynamics (CFD) simulation was carried out. The air distribution, wind speed, temperature, relative humidity, and predicted mean vote (PMV) in the seating area were analyzed under three operation models. The first boundary condition was with only underfloor air-conditioning working, the second was with underfloor airconditioning working with side air-conditioning, and the third was with only the side air-conditioning working. According to the simulation result, the temperature is distributed evenly, and the PMV in the auditorium seating area is generally between −0.5 and 0.5 under the first boundary condition. Operating with underfloor air-conditioning and side airconditioning, the temperature on the first floor can be kept at a lower level, which is suitable for banquet events. However, if only side air-conditioning is operated, the temperature on the first floor is relatively higher than in other conditions. The natural ventilation in the theater is influenced by its complex layout. According to the Green Building Evaluation Rules (GB 50736), a well-ventilated space is defined as one where at least 60% of the area experiences an average of two natural ventilation changes per hour during a typical transition season. Effective natural ventilation is associated with factors such as the building’s plan layout, the positioning of openings, and the depth of space. Based on the simulations, a number of additional windows were added to the theater, as outlined in Table 2.

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Table 2 Newly added window of the theater Floor

Newly added window dimension

The number of new windows added

1F

–

0

2F

1200 * 1500

11

3F

0915 * 1220

15

4F

0915 * 610

10

5F

–

0

4.3 Indoor Air Quality There has been a growing interest in understanding the associations between human health and the indoor environment, especially after the COVID-19 pandemic [5]. Indoor pollutants and particulates can be brought into or created in indoor areas by openings, occupant activities, and ventilation systems. Indoor air pollution can lead to health problems, odors, discomforts and irritation, diseases, cancer, and even increased human mortality [3]. All areas within the project have been equipped with outdoor air units to provide a fresh air supply, which satisfies the requirements stated within the ASHRAE Standard 62.1-2010. With considerations towards the outdoor air quality in Shanghai, all airhandling units (AHU) for fresh air have been equipped with Class F7 filters. CO2 monitoring was set up in high-traffic areas, and was linked with the ventilation system. When the CO2 concentration exceeds the set limitation, an alarm will be triggered, and the outdoor air units in that area will be activated immediately to increase the supply of fresh air. In addition to mechanical ventilation, natural ventilation is known as a passive design method and is a facilitated way to use renewable wind energy. For this reason, natural ventilation is considered one of the fundamental solutions to reduce energy consumption in buildings as well as improving the indoor environment [9]. Shanghai experiences four distinct seasons, with a humid subtropical climate that is uncomfortable in summer and winter but pleasant in spring and fall, which are the best seasons for natural ventilation. The design of the commercial and cultural center used CFD simulation to find sufficient openings with optimized locations to improve the natural ventilation of the commercial area during the transition seasons. The hotel and office curtain walls have also been equipped with openable windows to make full use of natural ventilation, ensuring that the average air change rate of the main functional rooms reaches twice an hour or more. Sufficient air changes can replace the air in the building during non-air-conditioned seasons, reducing the risk of pollutant accumulation. During the transition seasons, utilizing natural ventilation also guarantees improved indoor air quality and thermal comfort. The project’s natural ventilation during the transitional seasons has been optimized. By simulating wind direction and pressure, we determined the optimal positions and sizes for the openings. The curtain wall of the office and commercial

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Fig. 5 Natural ventilation simulation of the hotel

building were designed with an openable area of 5%, while each hotel room has at least one openable window. The simulation result of the hotel is shown in Fig. 5.

5 Conclusions This chapter outlines a commercial complex project in Shanghai, designed by Shanghai TIANHUA Architecture Planning & Engineering Ltd. Its sustainable design strategies are presented across three critical aspects: site design, resource conservation, and indoor environmental quality. It is believed that TIANHUA will continue to implement such sustainable development designs, with an emphasis on resource conservation, in their future projects to contribute to the goal of carbon neutrality.

References 1. Arnfield AJ (2003) Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island. Int J Climatol 23 2. Ayusiey RM (1989) Politics of pedestrian level urban wind control. Build Environ 24(4):290– 295 3. Bhat MA et al (2022) Investigation of indoor and outdoor air quality in a university campus during COVID-19 lock down period. Build Environ 219(1):109176 4. Chan F et al (2022) Meeting financial challenge facing China’s sponge city program (SCP) – Hong Kong as a gateway to green finance. Nat Based Solut 2:100019 5. Cheng Z et al (2022) Investigations of indoor air quality for office buildings in different climate zones of China by subjective survey and field measurement. Build Environ 214(15):108899

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6. Chow D (2017) Indoor environmental quality: thermal comfort. In: Encyclopedia of sustainable technologies, pp 209–219 7. EPA. Benefits of low impact development. https://www.epa.gov/sites/default/files/2015-09/doc uments/bbfs1benefits.pdf. Accessed 11 November 2022 8. Fletcher T et al (2014) SUDS, LID, BMPs, WSUD and more—The evolution and application of terminology surrounding urban drainage. Urban Water J 12(7):525–542 9. Ghalam N et al (2021) Investigation of optimal natural ventilation in residential complexes design for temperate and humid climates. Sustain Energy Grids Netw 27:100500 10. Grimm NB et al (2008) Global change and the ecology of cities. Science 319:756–760 11. Hong B, Lin B (2015) Numerical studies of the outdoor wind environment and thermal comfort at pedestrian level in housing blocks with different building layout patterns and trees arrangement. Renew Energy 12. Ishaq S et al (2019) State of provincial regulations and guidelines to promote low impact development (LID) alternatives across Canada: content analysis and comparative assessment. J Environ Manag 235:389–402 13. Jenkins K et al (2017) Crick assessing surface water flood risk and management strategies under future climate change: insights from an agent-based model. Sci Total Environ 595:159–168 14. Johansson T et al (2017) Development of an energy atlas for renovation of the multifamily building stock in Sweden. Appl Energy 203:723–736 15. Li J et al (2022) Exploring urban space quantitative indicators associated with outdoor ventilation potential. Sustain Cities Soc 79:103696 16. Li ML et al (2022) Discussion on water saving system of public buildings and its influencing factors. 17. Liu D (2016) China’s sponge cities to soak up rainwater. Nature 537:307 18. Mao Z (2022) Research on the influence of power enterprise behavior on enterprise value under the background of carbon emission reduction 19. Oke T (1982) The energetic basis of the urban heat island. Q J R Meteorol Soc 108:1–24 20. Ramsay EE et al (2021) Chronic heat stress in tropical urban informal settlements. iScience, 24. Article 103248 21. Sarigiannis et al (2019) Indoor air and public health. Management of emerging public health issues and risks. In: Multidisciplinary approaches to the changing environment, pp 3–29 22. Tibbetts M (2011) Quantitative study on sustainability of water resources utilization pattern in Shanghai. 23. Xue P et al (2016) Quantification of luminous comfort with dynamic daylight metrics in residential buildings. Energy Build 117:99–108. ISSN 0378-7788 24. Zhao L et al (2018) Interactions between urban heat islands and heat waves. Environ Res Lett 13. Article 034003 25. Zhao ZT et al (2022) An urban-ecological-feature based assessment system for sponge city construction. Environ Sci Eco-Technol 12:100188

An Integrated and Intelligent Information Model-Based Smart University Campus and Its Digitalization Process Georgios Kapogiannis, Nan Lu, Cesar Augusto, Thapa Sudhir, Ravil Misalimov, Novianti, and Tianlun Yang

Abstract A smart university campus implementation plan is the integration of the Internet of Things (IoT), Common Data Environment (CDE), Cloud Computing, Data Mining and other digital technologies to enhance campus management. In the article, the researchers are focusing on a wide variety of technologies, integrating them with different hardware and software, to find their impact on a smart city and their contribution to the digitalization process. The research uses a literature review to explore current strategies for smart campus services, and uses an experiment on developing a digital UNNC campus. The research finds that digital transformation can be achieved through the integration of various hardware and software, especially under the condition of the digital culture in China. The findings of this research show that digital transformation can improve facility management and enhance operation capability. Keywords SmartMart campus · Digitalization · IoT · BIM

1 Introduction With the development of universities in the digital era comes the divergence between the traditional operation and maintenance of campuses and their new functional requirements in terms of increasing costs and energy consumption, as well as increased risks. The new relationship between the campus and its users is reliant G. Kapogiannis (B) School of Business and Leadership, Oryx Universal College in partnership with Liverpool John Moores University, Doha, Qatar e-mail: [email protected] N. Lu · C. Augusto · T. Sudhir · R. Misalimov · Novianti University of Nottingham Ningbo China, Ningbo, China T. Yang NingboTech University, Ningbo, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 T. Zhou et al. (eds.), Smart Buildings and Technologies for Sustainable Cities in China, Urban Sustainability, https://doi.org/10.1007/978-981-99-6391-1_12

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on the collection, digitalization and processing of data from IoT sensors, among other sources. To provide a holistic approach to improve the standard of living and the sustainability of the campus environment for students and staff, timely information is key [7]. The purpose of this research is to investigate digital transformation in order to support asset and facilities management. The implementation of a smart campus requires integrated management, where the various departments cooperate among themselves, resulting in the reduction of misunderstandings during operation through the visualization of a digital campus. Nowadays, the University of Nottingham Ningbo China (UNNC) is working towards sustainable development and is implementing the smart city concept on the whole campus. The main goal is to provide living and working conditions which are conducive to student and staff participation in academic life, thereby enriching the university heritage, enhancing multicultural collaboration, and developing the spirit of innovation [12, 16]. The transformation into a smart campus entails creating a complex ecosystem based on technological advances. In order to make the campus more livable, green and inclusive, focus on the human being as the main agent of transformation [16]. The purpose is to optimize the standard of living of those who constantly make use of the infrastructure, buildings, network, technology, etc., to improve competitive advantages, add value to the university and simultaneously reduce management expenditure [22]. Furthermore, smart campuses have stronger and more efficient security systems in the face of natural hazards or security risks [12].

2 Research Aim, Objectives, and Research Design Facility management needs to take into consideration a multitude of factors. For example, as the quantity of building data increases, so do the challenges of monitoring it. UNNC campus has large numbers of buildings, which is difficult to be managed and demands the intervention of big data. Therefore, this research aims to determine whether the implementation of digital technologies can enhance facility management capability. An additional aim of this research is to investigate the impact of the digital transformation of the university campus on facilities management. Three research objectives are: (a) evaluate the role of digital technologies, both hardware and software; (b) explore the way these digital technologies are integrated; (c) investigate how the integrated digital technologies can enhance facility management capability from a smart campus perspective. The findings of this research can be applied to many other similar types of communities, for example, villages and small towns. The research uses a literature review to identify the key impact of digital technologies, and then conducts an experiment by using the UNNC campus to test the contribution of these technologies. There are two main stages (see Figs. 1 and 2) of the smart campus implementation strategy: the first stage focuses on the process of gathering information continuously from staff and students to identify the main assumptions aligned with the vision and assessment of the business scope; the second

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Fig. 1 Understand the demands and requirements

Fig. 2 Developing process of the smart campus

stage is divided into three main sections, which are data collection, data processing, and data visualization. Three main types of data collected for processing are existing data from currently archived documentation, geo-spatial data, and sensor data. During the data processing, researchers integrated IoT and digital models into a Common Data Environment (CDE), also VR/AR/MR, AI and Digital Twins are integrated into a CDE for the data visualization process (see Fig. 2).

3 Impact of Digital Technologies The importance of BIM application in FM (Facility Management) has been noted in both industry and academia, for example, real-time data collection technologies, such as air quality sensors, which can be integrated with the BIM model to monitor real-time data on air pollution. Adams et al. [2] and Yang et al. [20] have studied

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solutions for indoor real-time position and navigation by using integrated sensors and radar. In addition, Radio Frequency Identification (RFID) is considered an efficient and cost-effective method of tracking equipment and facilities, such as furniture, by means of tags which can be read by machines [11]. BIM, therefore, represents a practical and effective method of implementing facility maintenance and campus management [19]. Several previous studies have presented different approaches to developing campus management through cutting-edge FMM (Facility Maintenance Management) technology, such as IoT deployment and data visualization [21], AR systems [10] and BIM-based automatic maintenance [9]. With increasingly advanced technology deployed in campus FMM, problems associated with traditional FMM such as high cost and the difficulty of inspection can be solved. What is more, future FMM will facilitate fully automated and zero-error management. With the intervention of immersive visualization technology such as VR, AR, and MR, a digital twin can be effectively visualized for improving the facility management performance [15]. A digital twin can represent the physical world via a virtual environment, and the simulation from the virtual world can support decision-making in the physical world [8]. Yasmin et al. [21] presented a framework for FMM which combines IoT and LoRaWAN networks with AR visualization collecting real-time information from sensors applied to users. An AR system has also been used for real-time RSS measurement collected by an AR device to visualize hidden devices to support FMM [10]. In addition, Abdelalim and his partners proposed using bar graphs and Sankey diagrams to show annual energy consumption [1]. Additionally, ArcGIS 10.3 was applied in analyzing and visualizing campus CO2 emissions to improve sustainability [3]. These studies focused on data visualization and utilized different methods, without an integrated digital platform to coordinate different types of data. Apart from data analysis, Chen et al. [9] proposed a BIM-based framework to schedule facility management, with automatic computation to generate the most efficient maintenance path according to the position of components failure, entrances and warehouses. Finally, Pan et al. [18] presented a BIM-based framework for a facility repair platform to trace information on the facility through code scanning. All in all, previous research has been oriented towards the analysis and visualization of different types of data for smart campus management, and there is a lack of research on integrated digital platforms. Therefore, in this research, the process of digital transformation of whole-campus information, including that from buildings, facilities and roads, is developed to integrate different types of data for FMM.

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4 Smart University Campus Development 4.1 System Architecture of the Smart Campus Figure 3 shows the system architecture of the smart campus experiment. In this experiment, three campus buildings are taken for modelling in their correct geo-location, matching up the coordinates in Autodesk Revit and determining digital terrain model, drainage design and flood analysis in Civil3D and HEC-RAS. Integration of all these performed tasks is achieved through one platform, InfraWorks. Following this is the formation of campus digital twin by using AI and Big Data. This is recorded using various types of IoT sensors and makes use of a cloud computing platform which is Microsoft Azure and the common data environment which is BIM360. All the models are available in BIM360, and through the use of Microsoft Azure’s various services and resources, models are simulated and digitalized, resulting in real-time analytic and stream processing of the campus. For security purposes, the campus is plugged into the Web Security Service (WSS) from the main campus data server or using mobile or remote services from remote sites. This security service is extremely secure, capable of protecting campus data from several threats, securing information, and preventing data loss across all devices. The unifying bridge or gateway between the web and the cloud is WSS; this ensures consistent enforcement of policies across all types of devices and even locations. Once the fundamental platform is set up real-time data can be collected through BIM360, which stores all the equipment data from the campus in order to link those physical aspects to the 3D model and obtain sensor data, where available, from the Azure IoT Hub by using Microsoft Azure Logic Apps [4]. In addition, each equipment coordinate connects the BIM model to the physical world. After completing the process, the relevant department can utilize the workflow chart to look back at any of the steps in order to make any necessary adjustments. The chart is structured as a cycle that allows the user to go back and forth in the process, according to needs or issues. In the chart, the coordinates are determined using QGIS and the campus area is mapped to include all infrastructure. Other GIS software, such as Google Earth, eCognition, Global Mapper, etc., is also used and can be utilized as needed. The mapped GIS data of the campus is used in every other platform as a base for performing tasks such as the placement of the buildings.

4.2 Data Collection and Geospatial Engineering—Based Simulation The collection of information for this experiment focuses on campus buildings and facilities and is undertaken with the permission of the campus authorities. QGIS, an open-source GIS platform has enabled the determination of the map projection for the project and mapping of the surroundings (buildings, streets, water bodies, boundary,

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Fig. 3 The digitalization process of the smart campus

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Mapping of the UNNC Campus Surroundings Using QGIS.

A Water Body with its Pan Direction Attribute Feature in QGIS.

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Road Attribute Feature.

Fig. 4 QGIS process

etc.) of the UNNC campus by utilizing the satellite imagery of OpenStreetMap’s projection function and files. The map projection used throughout this whole project has been set to Universal Transverse Mercator (UTM,) which lies at 51N (wgs84; EPSG-3651). The buildings’ attributes are included to provide the number of floors, rooms, staff, head capacity (persons), area, height, length, breadth and other important data. Roads have also been included individually in the attributes table which describes the total length, total width, and crowd level of each road present in the campus vicinity. Water bodies and vegetation present on the campus are also mapped out accordingly (see Fig. 4). Additionally, Autodesk Recap enables the viewing of laser-scanned data by extracting thousands of point clouds. The software allows the processing and storage of a large amount of laser-scanned data by leveraging cloud computing along with 3D mesh creation. The geospatial information collected in Recap can be used on QGIS to characterize elements such as buildings and roads. Google Earth is a simple tool used for obtaining building coordinates used during the merge of BIM models with the mapped GIS layer (see Fig. 5). At least two corners of the building’s coordinates have been taken for this step to be completed.

4.3 Data Processing to Visual Information: Flooding Simulation This experiment uses 3D Civil modelling which supports civil infrastructure BIM workflows in 3D. The application is feasible to evaluate the impact of floods by using the established 3D sanitary sewers model and to build the correct topography by following the integrated information. Once the coordinates system has been set up in the GIS process, the next step is downloading the Digital Terrain Model (DTM) image from the satellite Alos-Palsar on the Alaska Satellite Webpage [17]. At the

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Distance measurement of roads in Autodesk Recap from the UAV Data

Determining coordinate points in Google Earth for extracting as a *.kml file to QGIS

Measurement of height of the SEB building in Autodesk Recap

Latitude and longitude determination in QGIS using the *.kml file which has been changed to a shapefile and the attribute features have been placed accordingly as Shown. UTM 51 N wgs84 EPSG3651.

Fig. 5 Recap and Google earth process

same time, a satellite image with high spatial resolution has been downloaded from Google Earth. The DTM and satellite image were used to analyze the campus territory with SAGAGIS (see Fig. 6), and it was possible to identify the areas with poor surface drainage that tend to accumulate water using HEC-RAS’s slope and terrain geometry function. Also, both images were valuable in creating the civil 3D model of pipe infrastructure around campus. This software allowed refinement of the surface to more realistically represent the terrain. Finally, the refined DTM was a very valuable input for the flood modulation, simulated using hydrological data. It is important to highlight that all this processing and data is geo-referenced. Autodesk Revit is a BIM-driven tool that allows the production of documentation and project design from the early concepts to the visualization of the final model. Revit provides tools for developing architectural, structural and MEP 3D models for free integration and collaboration between these teams (see Fig. 7). Following the

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DEM and satellite images with the same size

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Contour lines from DEM

Fig. 6 DTM outline process

modelling stage, each model was coordinated for integration. Revit allows the determination of the direction and coordinates in the UTM coordinate system individually for each model. Therefore, the different buildings were coordinated into one model with a common system of coordinates. Each model can be modified individually in its own file with changes automatically uploaded to the coordinated model. Since Revit 2019, Civil 3D topography can be imported to Revit and used by architects and engineers. The coordinated model was exported to InfraWorks for data visualization (see Fig. 8).

Fig. 7 Infrastructure model process

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Fig. 8 Coordinated model in Autodesk Revit

4.4 Data Integration and Visualisation of the Smart Campus InfraWorks is powerful software that allows the user to compile and visualize data from GIS and BIM. In this project, InfraWorks is used to compile all models generated using QGIS, Civil 3D and Revit. The first step is to set the coordinates and scope of the project area, following which the terrain model (ground elevation, roads, building coordinates) is loaded. InfraWorks is capable of generating building models when there is no specific model available, but the result is not a detailed model, just sufficient for visual presentation. Additionally, the location and sizes of the buildings were generated from the shape files generated using GIS. A more detailed building model can also be loaded from Revit; in this project, three buildings were modelled in Revit. The building coordinates were set in Revit with the same coordinate system used in InfraWorks. Once the drainage system from Civil 3D was added, missing elements such as trees, furniture, people, etc., were added manually in InfraWorks (see Fig. 9). Changes for roads, bridges, and drainage design can be affected directly in InfraWorks and the data from the model exported to other software such as Civil 3D [5]. To simplify management, all the model information is stored in BIM 360. This is important because when some aspect of the model needs to be updated in the respective software, as long as the files in BIM 360 are also renewed, the model in InfraWorks can be refreshed to show the update [6]. Additionally, the models above are linked to BIM 360 to ensure access for collaboration among different experts (see Fig. 9). The model can be opened by those with access and edited by those with authorization [6].

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Compiled model in InfraWorks

Building generated using InfraWorks

Centre of Sustainable Environment Technology (CSET) building model loaded

Modelled drainage system

Extra utilities added to the environment.

View of the InfraWorks model in BIM 360

Fig. 9 InfraWorks process

5 Findings The findings of the research show that a smart campus requires the BIM LOD 350 model to enhance campus management, which consists of information and data on architecture, infrastructure, and facilities. The developed campus information model is achieved with the following attributes for the innovation: ● Visualization of Campus Information Modelling and BIM. ● Easier holistic and comprehensive understanding of asset management. ● Analysis of the flooding model to prevent potential risks and simulation using VR/MR. ● Energy performance analysis of campus buildings to realize smart control of energy performance. ● Digitalization of asset management and smart facilities management such as a smart inventory.

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With the implementation of a smart campus, the process of decision-making is more effective and efficient through the huge amount of data which allows monitoring of the campus’s current conditions. It also makes for closer relationships between people through data availability and transparency, increasing the opportunities for stakeholders to provide feedback. It enhances public life and makes the campus more attractive for students, staff and visitors. Furthermore, the development of facilities such as WiFi 6, 5G, internet and digital devices allows people to have better integration with the digital environment. The adoption of renewable energy and energy-efficient buildings, and implementation of waste segregation can significantly mitigate adverse environmental impacts. Furthermore, employing energy-efficient control systems and enhancing air conditioning quality through sensor technology should contribute to this effort as well. Moreover, during the modernization process, the infrastructure on campus will be improved. Smart sensors will also be able to detect damage, and the data collected may be useful in identifying risks and problemsolving. In short, it will significantly improve the quality of life on campus. The present report builds geographical, terrain, infrastructure and architecture models using QGIS, Civil 3D and Revit respectively, and then integrates these models using Autodesk and InfraWorks. In this case, the 3D campus model comprises geographically coordinated data, terrain data, and infrastructure data such as landscape and conduit layout, conduit layout and building information about position, structure, assets, HVAC, MEP, etc. Additionally, this project analyzes smart library energy performance, as well as flood performance, to help design infrastructure and improve maintenance. Furthermore, real-time data collection through sensors is presented in the report to monitor overall campus performance. The campus information model realizes smart maintenance, smart management and smart mobility that aims to enhance the campus experience. The digital transformation of facility management requires the input of multiple technologies from both hardware and software perspectives, but the rapid development of digital technologies makes timely replacement difficult. Due to the existence of the BIM paradigm, people, processes, and technologies are inextricably linked [13]. Therefore, the implementation of digital transformation also needs to take into account the process from Standard, Method, and Procedure (SMPs) perspectives for ensuring the quality of facility management. Moreover, the criteria of information management under ISO 19650 (2018) also highlight the importance of SMPs. Hence, technologies do not stand alone; instead, there is an increasing need to cooperate to develop suitable solutions for their successful implementations ensuring the development of a collaborative culture [14].

6 Limitations and Future Research The biggest limitation of this research is its small sample size, all of the data collection, modelling, and management was conducted within a single university campus. Since a smart city or regional modelling requires massive data collection, obtaining

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the permission necessary to avoid legal issues is a lengthy and complicated procedure, and data collection is prohibited in some areas. Therefore, although the experiment shows a successful digital transformation, it is not easy to be extended to the whole society. There were two other notable limitations during the experiments in this research. Firstly, energy and flood data cannot be visualized in real-time. Secondly, sensor data cannot be expressed in real-time. Therefore, future research will focus more on visualization for enhancing the performance of real-time data on information models to support facility management.

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