Adaptive Thermal Comfort of Indoor Environment for Residential Buildings: Efficient Strategy for Saving Energy (SpringerBriefs in Architectural Design and Technology) [1st ed. 2021] 9811609055, 9789811609053

Citation preview

SPRINGER BRIEFS IN ARCHITEC TURAL DESIGN AND TECHNOLOGY

David Bienvenido-Huertas Carlos Rubio-Bellido

Adaptive Thermal Comfort of Indoor Environment for Residential Buildings Efficient Strategy for Saving Energy

SpringerBriefs in Architectural Design and Technology Series Editor Thomas Schröpfer, Architecture and Sustainable Design, Singapore University of Technology and Design, Singapore, Singapore

Indexed by SCOPUS Understanding the complex relationship between design and technology is increasingly critical to the field of Architecture. The Springer Briefs in Architectural Design and Technology series provides accessible and comprehensive guides for all aspects of current architectural design relating to advances in technology including material science, material technology, structure and form, environmental strategies, building performance and energy, computer simulation and modeling, digital fabrication, and advanced building processes. The series features leading international experts from academia and practice who provide in-depth knowledge on all aspects of integrating architectural design with technical and environmental building solutions towards the challenges of a better world. Provocative and inspirational, each volume in the Series aims to stimulate theoretical and creative advances and question the outcome of technical innovations as well as the far-reaching social, cultural, and environmental challenges that present themselves to architectural design today. Each brief asks why things are as they are, traces the latest trends and provides penetrating, insightful and in-depth views of current topics of architectural design. Springer Briefs in Architectural Design and Technology provides must-have, cutting-edge content that becomes an essential reference for academics, practitioners, and students of Architecture worldwide.

More information about this series at http://www.springer.com/series/13482

David Bienvenido-Huertas · Carlos Rubio-Bellido

Adaptive Thermal Comfort of Indoor Environment for Residential Buildings Efficient Strategy for Saving Energy

David Bienvenido-Huertas Higher Technical School of Building Engineering University of Seville Seville, Spain

Carlos Rubio-Bellido Higher Technical School of Building Engineering University of Seville Seville, Spain

ISSN 2199-580X ISSN 2199-5818 (electronic) SpringerBriefs in Architectural Design and Technology ISBN 978-981-16-0905-3 ISBN 978-981-16-0906-0 (eBook) https://doi.org/10.1007/978-981-16-0906-0 © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 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

Contents

1 Building Energy Efficiency and Sustainability . . . . . . . . . . . . . . . . . . . . . . 1.1 Low-Carbon Economy Goals and Energy Efficiency in Buildings . . . 1.2 Value Drivers of Building Energy Efficiency Projects . . . . . . . . . . . . . 1.3 Regulatory Framework for Building Energy Efficiency . . . . . . . . . . . . 1.4 Energy Poverty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 1 4 5 6 7

2 Adaptive Thermal Comfort Models for Buildings . . . . . . . . . . . . . . . . . . . 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Steps Before the Adaptive Model: The Static Thermal Comfort Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 ASHRAE 55-2017 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 EN 16798-1:2019 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Other Adaptive Thermal Comfort Models . . . . . . . . . . . . . . . . . . . . . . . 2.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

13 13 14 16 18 20 31 31

3 Application of Adaptive Thermal Comfort Models for Energy Saving in Buildings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Adaptive Natural Ventilation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Adaptive Setpoint Temperatures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Barriers and Opportunities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

35 35 36 37 40 47 48

4 Energy Savings Obtained with an Adaptive Approach with Respect to Building Envelope Improvement . . . . . . . . . . . . . . . . . . . 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Energy Saving with Adaptive Measures . . . . . . . . . . . . . . . . . . . . . . . . .

51 51 52 58

v

vi

Contents

4.4 Combination of Adaptive Measures with Other Energy Conservation Measures: Payback Periods . . . . . . . . . . . . . . . . . . . . . . . 62 4.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 5 Decision-Making in Applying Adaptive Approaches in Indoor Spaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Decision-Making Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 The Single Criteria Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.2 The Multi-criteria Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

69 69 70 70 71 75 75

Chapter 1

Building Energy Efficiency and Sustainability

Abstract Architects and engineers play a key role in the transformation of the building sector toward energy efficiency and climate change mitigation. Buildings are responsible for 40% of the total energy consumption and 11.9% of the CO2 emission. In this sense, various international agreements are focused to decarbonize buildings, and the concept of nearly Zero-Energy Buildings (nZEB) as well as the implementation of Energy Performance Certificates (EPCs) have been designed. For that reason, building energy efficiency projects could be a driving force to achieve a low-carbon building stock and Energy Poverty (EP) mitigation. This chapter considers energy improvements and comfortable indoor spaces, in which the most appropriate operational guidelines and the users’ training measures are crucial. With this approach, adaptive thermal comfort models could be an opportunity to guarantee sustainable use of Heating, Ventilating, and Air Conditioning (HVAC) systems without affecting users’ thermal comfort. This paves the way for significant reductions in energy consumption with a more responsible use of HVAC systems considering the adaptive thermal comfort models. Keywords Energy efficiency · Climate change · Nearly zero-energy building · Energy performance certificate · Energy poverty · Building sector · Thermal comfort · Adaptive comfort

1.1 Low-Carbon Economy Goals and Energy Efficiency in Buildings Why are architects and engineers essential to decarbonize society? Various answers are possible, but the most direct is the potential of technicians to achieve a sustainable balance between citizens’ activity and the natural environment, and this is due to the current environmental situation. The natural environment is today in a continuous process of environmental degradation that contributes to environmental pollution, sea-level rise, and more and more extreme temperatures [71]. In addition, the greatest anthropogenic activity is depleting the Earth’s natural resources. Some reports of the World Wildlife Fund (WWF) indicate that the capacity of these resources was © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 D. Bienvenido-Huertas and C. Rubio-Bellido, Adaptive Thermal Comfort of Indoor Environment for Residential Buildings, SpringerBriefs in Architectural Design and Technology, https://doi.org/10.1007/978-981-16-0906-0_1

1

2

1 Building Energy Efficiency and Sustainability

exceeded 40 years ago [71]. Regardless of this aspect, climate change will lead future generations to live in a more extreme habitat. This was reflected in the fifth report of the Intergovernmental Panel on Climate Change [33]. The Representative Concentration Pathway scenarios show that temperatures worldwide will increase between 0.3 and 4.8 °C by the end of the twenty-first century and, together with other problems such as droughts and sea-level rise, will affect environmentally, socially, and politically, aspects difficult to be quantified today. The main reason for this is the high concentration of greenhouse gas emissions in the atmosphere, such as CO2 , CH4 , and N2 O [70]. That concentration leads to the atmosphere being less capable of reflecting solar radiation and of evacuating heat, thus, increasing external temperatures will increase. Among all gases, CO2 stands out because its emissions have significantly increased: today it annually increases by 1%, with percentages greater than 146% in comparison with the emissions generated in preindustrial times [70]. Thus, greenhouse gas emissions should be drastically reduced. At this point, the impact caused by buildings is crucial due to their outstanding role to mitigate climate change [37, 75]. In this regard, society is partly aware of the impact caused by various sectors, such as transport. However, consumption data show that energy consumption in buildings is significant in comparison with other sectors. Figure 1.1 shows the average estimates of the annual greenhouse gas emissions of various sectors in the European Union. Among the anthropogenic sectors with the greatest environmental impact, the building sector is in the fourth position (after those strongly related to the environmental degradation, such as industry (or transport). Moreover, these data could be greater according to the source consulted, that is, recording values of 38% in the building sector [66]. The reason for this fourth position is mainly its high energy consumption. It is estimated that 40% of the total energy consumption from anthropogenic activities is related to the building sector [50]. In 2010, the total energy consumption of this sector was 23.7 PWh, and it could reach 38.4 PWh by 2040 [34]. The residential building energy consumption in the European Union was responsible for 25.7% of the total primary energy consumption in 2016 [20]. Moreover, the total

Fig. 1.1 Average emission of CO2 between 1990 and 2016 in the European Union by the productive sector. Figure drawn with the data from the European Environment Agency [20]

1.1 Low-Carbon Economy Goals and Energy Efficiency in Buildings

3

Fig. 1.2 Tendency of the CO2 emissions caused by the building sector in the European Union from 1990 to 2016. Figure drawn with the data of the European Environment Agency [20]

energy consumption annually has been increasing by 1% since 1990, with a peak of 2.5% in relation to the electric demand [20]. However, this increase in the electric demand did not increase greenhouse gas emissions. Although the emissions annually caused by the building sector have been greater than 500 million tons in the region from 1990 to date (Fig. 1.2), there is a progressive downward trend that can be the result of the energy efficiency policies implemented in recent years. This brings hope to achieve the decarbonization goals of the building stock. Nonetheless, the risk that greenhouse gas emissions could lead to turning points implies the need to establish a greater reduction of energy consumption and greenhouse gas emissions. Under this circumstance, there is a growing international awareness of the need for reducing these emissions. The Paris Agreement was useful to establish a commitment among 195 countries in relation to the development of policies to achieve progressively a low-carbon economy by 2050. As for the European Union, the roadmap for moving to a low-carbon economy set the goal of reducing the greenhouse gas emissions from the building sector by 90% by 2050 in comparison with the levels from 1990 [17]. Likewise, the Sustainable Development Goals (SDGs) of the United Nations also include the commitment to decarbonize buildings. More specifically, its 11th goal titled ‘Sustainable Cities and Communities’ aims to achieve that cities are sustainable and resilient, thus reducing their negative environmental impact. Thus, building energy performance should be improved, and architects and engineers play a crucial role in that. In addition, building energy improvement projects are a tool to achieve the SGDs proposed for the building sector [40].

4

1 Building Energy Efficiency and Sustainability

1.2 Value Drivers of Building Energy Efficiency Projects Building projects could be a driving force to achieve a low-carbon building stock. However, the design of buildings is becoming more and more complex for architects and engineers [3] because they require more complex technologies [51]. In the past, their technical expertise was focused on structural aspects of buildings (reinforced concrete structures, earthquake resistance, etc.); today, however, demands of sustainability, energy efficiency, thermal comfort, and indoor air quality are a great technical challenge [72]. For this reason, the problems and risks faced by architects and engineers in new efficient building projects will be greater than those faced in old building projects [30, 74], thus increasing their construction time [31]. Moreover, these projects could increase the construction cost, mainly because of three reasons: (i) the use of technologies with a high efficiency and sustainability potential whose cost is high [28, 39]; (ii) the intervention of a larger number of expert consultants in the design and construction process [49]; and (iii) the greater time required to complete the building [73]. Nonetheless, the design of energy-efficient buildings allows for goals beyond the improvement of the environment to be addressed. In this regard, various studies have specified the value drivers pursued with sustainable buildings [29, 41]: environmental, social, economic, health, and thermal comfort improvements. The environmental improvement is obvious. If building energy consumption is reduced, then greenhouse gas emissions are expected to be lower. Likewise, the use of sustainable materials could ensure that the environmental impact during construction is low [38]. This environmental improvement takes place along with an improvement of users’ comfort and health. Building energy efficiency projects should be understood not just as an improvement of users’ habitability conditions or the reduction of buildings’ environmental impacts, but also as having a direct impact on the buildings’ economic valuation [69]. Recent studies by the Royal Institution of Chartered Surveyors have stressed that building energy efficiency is becoming a value driver in financing building projects on the part of the banking sector [56]. Moreover, a better energy label of buildings contributes to a greater monetary value of dwellings. Some studies conducted in Italy found that buildings with a better energy label (A, B, or C) sell for a 6% higher price compared to buildings with a worse energy label (G) [9]. In addition, buildings with an ecological label LEED or ENERGY STAR increase their selling value by up to 30% [15, 24]. However, there is a problem in the value driver argument of building energy efficiency in terms of property valuation. Traditional value drivers relate to the visible aspects of buildings (e.g., appropriate glazing or the state of installations) and are more important in the assessment of buildings than energy data, such as the energy certification [56]. In the future, energy efficiency will become probably more important in the existing relationship between moneylenders and investors and will contribute to various installment loans [56].

1.2 Value Drivers of Building Energy Efficiency Projects

5

Finally, it is important to stress the value driver of building energy efficiency projects to reduce Energy Poverty (EP). A household is in EP when over 10% of its income is used to pay the energy bill [10]. The possibility of EP in a household therefore depends on the family income or its energy expenditure [11]. The level of users’ income and its possible variations that could take place in families (e.g., due to job loss of its members) could be unknown when designing a new building, so a value driver for energy efficiency projects is that these projects constitute instruments to reduce the possibility of EP cases in the future [27]. The acquisition of a dwelling unit with better energy performance ensures that a family is unlikely to be in EP in the future because of the dwelling unit’s energy performance.

1.3 Regulatory Framework for Building Energy Efficiency The regulatory framework is essential for countries to achieve sustainable goals [2]. Grob and McGregor [26] concluded that the environmental obligations included in the standards should be adapted to society’s current expectations and in a continuous evolution process. These policies are therefore crucial to implement sustainability goals adequately [52]. Moreover, they should be designed in a way that they can be implemented without any problems [54]. As mentioned above, the decarbonization goal of buildings should be reached by 2050. For this purpose, the concept of nearly Zero-Energy Buildings (nZEB) has been developed. This concept refers to buildings with very low energy consumption by combining envelope designs with a very low heat transmission, the use of energyefficient systems, and self-consumption with renewable energies [14]. The first step to achieve such buildings was established by Directive 2010/31/EU (also known as Energy Performance of Buildings Directive (EPBD)) [21], which is a consolidated version of a previous directive (Directive 2002/91/EC [18]) and was reviewed in 2012 with Directive 2012/27/EU [22]. This directive established that the countries of the European Union should mandate in their regulations that new or restored buildings are nZEB after the following dates: (i) January 1, 2019, for public buildings; and (ii) January 1, 2021, for all the remaining new buildings. The EPBD has been differently implemented in the various European countries. For example, the EPBD has been differently implemented in Spain according to its progressive modifications. Its first version (Directive 2002/91/EC) was implemented in 2006 in the Basic Energy Saving Document (DB-HE in Spanish) of the Spanish Technical Building Code [61], together with the Thermal Installations Regulation in Buildings (RITE in Spanish) [62]. Both regulations have been modified several times. The DB-HE was modified in 2013 both by superficially including nZEB and by increasing energy demands [63]. In 2019, however, nZEB was further implemented [64]. Moreover, the subsequent modifications increased the envelope thermal requirements [7]. Such evolutions can also be found in other European countries, such as Greece [25], Italy [55], Portugal [4], France [7], and Cyprus [23]. However, these developments do not imply an adequate implementation of the definition of nZEB

6

1 Building Energy Efficiency and Sustainability

in many European countries, particularly those in the South [6]. Thus, the regulation of these countries should evolve in the next years to achieve a better nZEB category. Likewise, other aspects developed in the EPBD were the Energy Performance Certificates (EPCs), which are among the main basic elements in the building energy analysis. EPCs are approximate reports of the energy efficiency and a compulsory implementation tool in European countries because of Directive 2002/91/EC [19]. In Spain, EPCs are compulsory for the selling or renting of old dwelling units, as well as for new or restored buildings. However, there is a gap between issuing EPCs and obtaining a building energy improvement [68]. EPCs are therefore not an effective tool in carrying out energy-efficient projects, and project specifications are required for all buildings. These project specifications are set up by first analyzing the characteristics of the envelope through energy audit works (e.g., infrared thermography or blower-door). After field data are compiled, an analysis is carried out by dynamic simulations in very robust energy calculation engines (e.g., EnergyPlus). In these analyses, operational and occupancy parameters can be adjusted to the actual parameters of the building, and the most appropriate Energy Conservation Measures (ECMs) can be determined to reduce the building energy consumption. In residential buildings, most energy efficiency projects are focused on reducing HVAC system energy use because these systems are usually the main contributors to overall energy consumption [35]. Nonetheless, it is worth stressing the existing huge gap to achieve today a lowcarbon building stock, mainly because most of the building stock of the European countries was built before the development of the first energy efficiency standards [42]. Thus, an energy restoration is required in all existing buildings. However, the current cycle of energy renovations is very slow [48], so it is doubtful that lowcarbon goals for the building stock can be achieved by 2050. In this regard, it could be difficult to guarantee that turning points could not be achieved in the climate even after reaching an annual renovation rate of 3%, as the European Union establishes (2012).

1.4 Energy Poverty This slow energy renovation process could lead to other social consequences including EP, a concept introduced by Isherwood and Hancock [36] and developed by Boardman [10]. A household is under an EP risk when more than 10% of its income is spent on building energy. This aspect means that EP is not related to lowincome families as it could also take place due to poor building energy performance [13, 53]. Moreover, it is worth stressing the implications of an excessive expenditure in energy consumption as it could imply that users try to face a larger number of thermal discomfort hours [58], thus adversely affecting people’s physical health [43, 46, 65]. Many studies have proven the existing relationship between EP and people’s risk of death [12, 44]. In a general sense, the impact of EP on people’s health in cold

1.4 Energy Poverty

7

zones has been established. Nonetheless, recent studies have stressed the impact of high temperatures on health [1, 47], thus implying a great risk for vulnerable families because of the time spent in their dwellings. In addition, lockdown episodes, such as the Covid-19 pandemic, [45], could force people to remain in their dwellings for long periods of time, so the EP risk increases. Today, EP takes place in most of the European demography. It is estimated that 124 million people in the European Union are under EP risk [5]. The policies on building energy improvement could play an essential role to reduce EP, apart from tackling climate change [16, 67]. However, this relationship is somehow limited because building energy consumption mainly depends on the respective families’ energy use. Thus, a renovated building could imply that greenhouse gas emissions are not improved and even worsened, mainly due to the rebound effects generated by the saving in the energy bill of dwellings (i.e., the new residents’ behavior in renovated buildings that increases their energy consumption [59, 60]). This aspect could be reflected in the increase in heating setpoint temperatures in winter [57]. Thus, building energy improvement is not the only measure to be considered by the energy policies of a country to reduce both greenhouse gas emissions and EP. Training measures should be established for citizens to know the most appropriate operational guidelines of Heating, Ventilating, and Air Conditioning (HVAC) systems to ensure thermal comfort in indoor spaces by consuming the least amount of energy possible. These types of policies are already established in some countries (e.g., the Super Cool Biz and Setsuden campaigns in Japan [8, 32]), but more countries should still develop them. In this scenario, adaptive thermal comfort models could be an opportunity to guarantee the sustainable use of HVAC systems without affecting users’ thermal comfort. Moreover, this could imply that, if a high energy renovation rate is not achieved in the following years, building energy consumption could be reduced by more responsibly using HVAC systems. The following chapters explore in detail the potential of using adaptive thermal comfort models as a strategy to reduce building energy consumption.

References 1. Abrahamson V, Wolf J, Lorenzoni I et al (2009) Perceptions of heatwave risks to health: interview-based study of older people in London and Norwich, UK. J Public Heal 31:119–126. https://doi.org/10.1093/pubmed/fdn102 2. Adham K, Siwar C (2012) Empirical investigation of government green procurement (GGP) practices in Malaysia. OIDA Int J Sustain Dev 4:77–88 3. Ahadzie DK, Proverbs DG, Sarkodie-Poku I (2014) Competencies required of project managers at the design phase of mass house building projects. Int J Proj Manag 32:958–969. https://doi. org/10.1016/j.ijproman.2013.10.015 4. Araújo C, Almeida M, Bragança L (2013) Analysis of some Portuguese thermal regulation parameters. Energy Build 58:141–150. https://doi.org/10.1016/j.enbuild.2012.11.024 5. Atanasiu B, Kontonasiou E, Mariottini F (2014) Alleviating fuel poverty in the EU: investing in home renovation, a sustainable and inclusive solution. Build Perform Inst Eur (BPIE), Brussels

8

1 Building Energy Efficiency and Sustainability

6. Attia S, Eleftheriou P, Xeni F et al (2017) Overview and future challenges of nearly zero energy buildings (nZEB) design in Southern Europe. Energy Build 155:439–458. https://doi.org/10. 1016/j.enbuild.2017.09.043 7. Bienvenido-Huertas D, Oliveira M, Rubio-Bellido C, Marín D (2019) A comparative analysis of the international regulation of thermal properties in building envelope. Sustainability 11:5574. https://doi.org/10.3390/su11205574 8. Bienvenido-Huertas D, Pulido-Arcas JA, Rubio-Bellido C, Pérez-Fargallo A (2020) Influence of future climate changes scenarios on the feasibility of the adaptive comfort model in Japan. Sustain Cities Soc 61:102303. https://doi.org/10.1016/j.scs.2020.102303 9. Bisello A, Antoniucci V, Marella G (2020) Measuring the price premium of energy efficiency: a two-step analysis in the Italian housing market. Energy Build 208:109670. https://doi.org/ 10.1016/j.enbuild.2019.109670 10. Boardman B (1991) Fuel poverty: from cold homes to affordable warmth. John Wiley & Sons Ltd, London, UK 11. Boemi SN, Papadopoulos AM (2019) Energy poverty and energy efficiency improvements: a longitudinal approach of the Hellenic households. Energy Build 197:242–250. https://doi.org/ 10.1016/j.enbuild.2019.05.027 12. Braubach M, Ferrand A (2013) Energy efficiency, housing, equity and health. Int J Public Heal 58:331–332. https://doi.org/10.1007/s00038-012-0441-2 13. Castaño-Rosa R, Solís-Guzmán J, Rubio-Bellido C, Marrero M (2019) Towards a multipleindicator approach to energy poverty in the European Union: a review. Energy Build 193:36–48. https://doi.org/10.1016/j.enbuild.2019.03.039 14. D’Agostino D, Zangheri P, Castellazzi L (2017) Towards nearly zero energy buildings in Europe: a focus on retrofit in non-residential buildings. Energies 10. https://doi.org/10.3390/ en10010117 15. Dermisi S, McDonald J (2011) Effect of “green” (LEED and ENERGY STAR) designation on prices/sf and transaction frequency: the Chicago office market. J Real Estate Portf Manag 17:39–52 16. Dubois G, Sovacool B, Aall C et al (2019) It starts at home? Climate policies targeting household consumption and behavioral decisions are key to low-carbon futures. Energy Res Soc Sci 52:144–158. https://doi.org/10.1016/j.erss.2019.02.001 17. European Commission (2011) A Roadmap for moving to a competitive low carbon economy in 2050. Belgium, Brussels 18. European Commission (2002) Directive 2002/91/EC of the European Parliament and of the council of 16 December 2002 on the energy performance of buildings. Brussels, Belgium 19. European Commission (2002) Directive 2002/91/EC of the European Parliament and of the council of 16 December 2002 on the energy performance of buildings. The Official Journal of the European Union, pp 65–71. https://doi.org/10.1039/ap9842100196 20. European Environment Agency (2018) Final energy consumption by sector and fuel (2016). Denmark, Copenhagen 21. European Union (2010) Directive 2010/31/EU of the European Parliament and of the council of 19 May 2010 on the energy performance of buildings. Belgium, Brussels 22. European Union (2012) Directive 2012/27/EU of the European Parliament and of the council of 25 October 2012 on energy efficiency, Amending Directives 2009/125/EC and 2010/30/EU and Repealing Directives 2004/8/EC and 2006/32/EC. Belgium, Brussels 23. Fokaides PA, Polycarpou K, Kalogirou S (2017) The impact of the implementation of the European energy performance of buildings directive on the European building stock: The case of the Cyprus Land Development Corporation. Energy Policy 111:1–8. https://doi.org/10.1016/ j.enpol.2017.09.009 24. Fuerst F, McAllister P (2011) Eco-labeling in commercial office markets: Do LEED and Energy Star offices obtain multiple premiums? Ecol Econ 70:1220–1230 25. Gaglia AG, Tsikaloudaki AG, Laskos CM et al (2017) The impact of the energy performance regulations’ updated on the construction technology, economics and energy aspects of new residential buildings: the case of Greece. Energy Build 155:225–237. https://doi.org/10.1016/ j.enbuild.2017.09.008

References

9

26. Grob S, McGregor I (2005) Sustainable organisational procurement: a progressive approach towards sustainable development. Int J Environ Work Employ 1:280–295. https://doi.org/10. 1504/IJEWE.2005.007488 27. Haley B, Gaede J, Winfield M, Love P (2020) From utility demand side management to lowcarbon transitions: opportunities and challenges for energy efficiency governance in a new era. Energy Res Soc Sci 59:101312. https://doi.org/10.1016/j.erss.2019.101312 28. Hand A, Zuo J, Xia B et al (2015) Are green project management practices applicable to traditional projects? In: Proceedings of the 19th International Symposium on Advancement of Construction Management and Real Estate. pp 291–301 29. Hwang B-G, Zhao X, Tan LLG (2015) Green building projects: Schedule performance, influential factors and solutions. Eng Constr Archit Manag. https://doi.org/10.1108/ECAM-07-20140095 30. Hwang B-G, Shan M, Supa’at NNB (2017) Green commercial building projects in Singapore: critical risk factors and mitigation measures. Sustain Cities Soc 30:237–247. https://doi.org/ 10.1016/j.scs.2017.01.020 31. Hwang BG, Leong LP (2013) Comparison of schedule delay and causal factors between traditional and green construction projects. Technol Econ Dev Econ 19:310–330. https://doi.org/ 10.3846/20294913.2013.798596 32. Indraganti M, Ooka R, Rijal HB (2013) Thermal comfort in offices in summer: Findings from a field study under the “setsuden” conditions in Tokyo, Japan. Build Environ 61:114–132. https:// doi.org/10.1016/j.buildenv.2012.12.008 33. Intergovernmental Panel on Climate Change (2014) Climate change 2014: synthesis report. Contribution of working groups I, II and III to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge 34. International Energy Agency (2013) World energy outlook 2013 35. International Energy Agency (2017) Energy efficiency 2017 36. Isherwood BC, Hancock RM (1979) Household expenditure on fuel: distributional aspects. London Econ Advis Off DHSS 37. Jo JH, Golden JS, Shin SW (2009) Incorporating built environment factors into climate change mitigation strategies for Seoul, South Korea: a sustainable urban systems framework. Habitat Int 33:267–275. https://doi.org/10.1016/j.habitatint.2008.10.020 38. John G, Clements-Croome D, Jeronimidis G (2005) Sustainable building solutions: a review of lessons from the natural world. Build Environ 40:319–328. https://doi.org/10.1016/j.buildenv. 2004.05.011 39. Kang Y, Kim C, Son H et al (2013) Comparison of preproject planning for green and conventional buildings. J Constr Eng Manag 139:4013018. https://doi.org/10.1061/(ASCE)CO.19437862.0000760 40. Karlsson F, Moshfegh B (2013) A low-energy building project in Sweden-the Lindås Pilot Project. Elsevier 41. Kibert CJ (2016) Sustainable construction: green building design and delivery. Wiley 42. Kurtz F, Monzón M, López-Mesa B (2015) Energy and acoustics related obsolescence of social housing of Spain’s post-war in less favoured urban areas. The case of Zaragoza. Inf la Construcción 67:m021. https://doi.org/10.3989/ic.14.062 43. Liddell C, Guiney C (2015) Living in a cold and damp home: frameworks for understanding impacts on mental well-being. Public Health 129:191–199. https://doi.org/10.1016/j.puhe. 2014.11.007 44. Liddell C, Morris C, Thomson H, Guiney C (2016) Excess winter deaths in 30 European countries 1980–2013: a critical review of methods. J Public Health (Bangkok) 38:806–814. https://doi.org/10.1093/pubmed/fdv184 45. Lima CKT, Carvalho PM de M, Lima I de AS et al (2020) The emotional impact of Coronavirus 2019-Ncov (New Coronavirus Disease). Psychiatry Res 287:112915. https://doi.org/10.1016/ j.psychres.2020.112915 46. Middlemiss L, Gillard R (2015) Fuel poverty from the bottom-up: characterising household energy vulnerability through the lived experience of the fuel poor. Energy Res Soc Sci 6:146– 154. https://doi.org/10.1016/j.erss.2015.02.001

10

1 Building Energy Efficiency and Sustainability

47. Ormandy D, Ezratty V (2016) Thermal discomfort and health: protecting the susceptible from excess cold and excess heat in housing. Adv Build Energy Res 10:84–98. https://doi.org/10. 1080/17512549.2015.1014845 48. Ortiz J, Salom J (2019) Health and related economic effects of residential energy retrofitting in Spain. Energy Policy 130:375–388. https://doi.org/10.1016/j.enpol.2019.04.013 49. Palanisamy P, Klotz L (2011) Delivery process attributes, common to India and the US, for more sustainable buildings. Coll Publ 6:146–157. https://doi.org/10.3992/jgb.6.4.146 50. Pérez-Lombard L, Ortiz J, Pout C (2008) A review on buildings energy consumption information. Energy Build 40:394–398. https://doi.org/10.1016/j.enbuild.2007.03.007 51. Prins M, Owen R (2010) Integrated design and delivery solutions. Archit Eng Des Manag 6:227–231. https://doi.org/10.3763/aedm.2010.IDDS0 52. Qi GY, Shen LY, Zeng SX, Jorge OJ (2010) The drivers for contractors’ green innovation: an industry perspective. J Clean Prod 18:1358–1365. https://doi.org/10.1016/j.jclepro.2010. 04.017 53. Rosenow J, Platt R, Flanagan B (2013) Fuel poverty and energy efficiency obligations-A critical assessment of the supplier obligation in the UK. Energy Policy 62:1194–1203. https://doi.org/ 10.1016/j.enpol.2013.07.103 54. Ruparathna R, Hewage K (2015) Sustainable procurement in the Canadian construction industry: current practices, drivers and opportunities. J Clean Prod 109:305–314. https://doi. org/10.1016/j.jclepro.2015.07.007 55. Salvalai G, Masera G, Sesana MM (2015) Italian local codes for energy efficiency of buildings: theoretical definition and experimental application to a residential case study. Renew Sustain Energy Rev 42:1245–1259. https://doi.org/10.1016/j.rser.2014.10.038 56. Sayce S, Wilkinson S (2019) Energy efficiency and residential values: a changing European landscape. RICS insight Pap 35 57. Seebauer S (2018) The psychology of rebound effects: explaining energy efficiency rebound behaviours with electric vehicles and building insulation in Austria. Energy Res Soc Sci 46:311– 320. https://doi.org/10.1016/j.erss.2018.08.006 58. Shortt N, Rugkåsa J (2007) “The walls were so damp and cold” fuel poverty and ill health in Northern Ireland: results from a housing intervention. Heal Place 13:99–110. https://doi.org/ 10.1016/j.healthplace.2005.10.004 59. Sorrell S (2015) Reducing energy demand: a review of issues, challenges and approaches. Renew Sustain Energy Rev 47:74–82. https://doi.org/10.1016/j.rser.2015.03.002 60. Sorrell S (2007) Improving the evidence base for energy policy: the role of systematic reviews. Energy Policy 35:1858–1871. https://doi.org/10.1016/j.enpol.2006.06.008 61. The Government of Spain (2006) Royal Decree 314/2006. Approving the Spanish Technical Building Code. Madrid, Spain 62. The Government of Spain (2007) Royal Decree 1027/2007 approving the Regulation of Thermal Installations in Buildings 63. The Government of Spain (2013) Order FOM/1635/2013 of September 10, updating Basic Document DB-HE « Energy Saving » of the Technical Building Code approved by Royal Decree 314/2006 of March 17 64. The Government of Spain (2019) Royal Decree 732/2019, of December 20, which modifies the Technical Building Code, approved by Royal Decree 314/2006, of March 17 65. Thomson H, Snell C (2013) Quantifying the prevalence of fuel poverty across the European Union. Energy Policy 52:563–572. https://doi.org/10.1016/j.enpol.2012.10.009 66. United Nations Environment Programme (2012) Building design and construction: forging resource efficiency and sustainable development. Sustain Build Clim Initiat 67. Ürge-Vorsatz D, Tirado Herrero S (2012) Building synergies between climate change mitigation and energy poverty alleviation. Energy Policy 49:83–90. https://doi.org/10.1016/j.enpol.2011. 11.093 68. Villca-Pozo M, Gonzales-Bustos JP (2019) Tax incentives to modernize the energy efficiency of the housing in Spain. Energy Policy 128:530–538. https://doi.org/10.1016/j.enpol.2019.01.031

References

11

69. Warren-Myers G, Kain C, Davidson K (2020) The wandering energy stars: The challenges of valuing energy efficiency in Australian housing. Energy Res Soc Sci 67:101505. https://doi. org/10.1016/j.erss.2020.101505 70. World Meteorological Organization (2019) United In Science 71. WWF (2014) Living Planet Report 2014: Species and spaces, people and places. Res accunting 1:180. https://doi.org/10.1007/s13398-014-0173-7.2 72. Xiao L, Bie L, Bai X (2021) Controlling the schedule risk in green building projects: buffer management framework with activity dependence. J Clean Prod 278:123852. https://doi.org/ 10.1016/j.jclepro.2020.123852 73. Zahirah N, Abidin NZ, Nuruddin AR (2013) Soft cost elements that affect developers’ decision to build green. Int J Civ Environ Eng 7:768–772. https://doi.org/10.5281/zenodo.1088592 74. Zhao X, Hwang BG, Gao Y (2016) A fuzzy synthetic evaluation approach for risk assessment: a case of Singapore’s green projects. J Clean Prod 115:203–213. https://doi.org/10.1016/j.jcl epro.2015.11.042 75. Zuo J, Read B, Pullen S, Shi Q (2012) Achieving carbon neutrality in commercial building developments-perceptions of the construction industry. Habitat Int 36:278–286. https://doi.org/ 10.1016/j.habitatint.2011.10.010

Chapter 2

Adaptive Thermal Comfort Models for Buildings

Abstract Thermal comfort has been widely studied from the middle of the twentieth century to the present. From Fanger’s model based on the neutral thermal state and the development of the Predicted Mean Vote (PMV) and Predicted Percentage of Dissatisfied (PPD) indexes to the adaptive approach based on buildings that operate with natural ventilation, several studies set detailed conditions with strengths and limitations. In this chapter, a review from the international to the specific comfort models is established. In this sense, ASHRAE 55-2017 and EN 16798-1:2019 are the two most used models; both are based on international research projects with large databases. The two models present similarities in terms of applicability, however, some differences are analyzed (e.g., categories considered). In an intermediary state, various countries like the Netherlands (ISSO 74) and China (GB/T50785) have developed specific adaptive thermal comfort models, which present sensible differences with the international standards. Moreover, local studies are carried out in Australia, Chile, India, and Romania, regarding specific building types (e.g., social dwellings in Chile) or for certain climate conditions. To sum up, many research studies at different levels of resolution have presented the potential of adaptive thermal comfort models, to better understand the users’ adaptability. Keywords Thermal comfort · Adaptive comfort · ASHRAE 55-2017 · EN 16798-1:2019 · Natural ventilation · Climate adaption

2.1 Introduction The energy performance of the existing building stock is deficient. The main energy consumption in buildings is currently the use of Heating, Ventilation and Air Conditioning (HVAC) systems to guarantee appropriate thermal comfort. Thus, users’ behavior strongly affects building energy performance and environmental impact. For this reason, the analysis of what is understood by thermal comfort and the existing models is crucial. Thermal comfort could be understood as the users’ subjective satisfaction in a thermal environment (American Society of Heating Refrigerating and Air Conditioning Engineers (ASHRAE) [3]). Nevertheless, there are limit conditions © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 D. Bienvenido-Huertas and C. Rubio-Bellido, Adaptive Thermal Comfort of Indoor Environment for Residential Buildings, SpringerBriefs in Architectural Design and Technology, https://doi.org/10.1007/978-981-16-0906-0_2

13

14

2 Adaptive Thermal Comfort Models for Buildings

Fig. 2.1 Timeline of thermal comfort models

that physiologically affect users. Thus, thermal discomfort limit conditions, such as those from heat waves or cold periods, could cause health problems in users [22, 26, 31] or even death [7], [23]. Given that the thermal properties of the existing building envelope have not allowed facing these thermal conditions, more and more buildings have HVAC systems to face periods with the greatest climate severity. As a result, thermal comfort has been widely studied from the middle of the twentieth century to date [21]. The first study on thermal comfort models was presented by Fanger in 1970 (also known as ‘static model’), and since then various international standards have been developed by including static and adaptive thermal comfort models Fig. 2.1. The following subsections deal with the existing approaches related to thermal comfort models, and adaptive thermal comfort models are broadly described.

2.2 Steps Before the Adaptive Model: The Static Thermal Comfort Model The first steps of thermal comfort models are included in the studies by Fanger [14]. Fanger designed a static thermal comfort model through surveys conducted in a controlled room. His studies are based on the theory of the neutral thermal state.

2.2 Steps Before the Adaptive Model: The Static Thermal Comfort Model

15

In addition, two indexes are calculated: Predicted Mean Vote (PMV) and Predicted Percentage of Dissatisfied (PPD). On the one hand, PMV determines the medium level of users’ thermal feeling. For this purpose, seven points are established on a scale Fig. 2.2. The thermal balance is obtained when users’ production and heat losses are the same (i.e., these aspects are balanced). Moreover, this heat balance can be affected by both personal factors (insulation of clothing and metabolic rate) and environmental factors (temperature, relative humidity, and airspeed). On the other hand, PPD determines the percentage of unsatisfied occupants and could be related to PMV Fig. 2.2. Regarding the values of the scale of PMV, the value of −3 corresponds to the too cold situation in the indoor space, and the value of +3 corresponds to the too hot situation. The static thermal comfort model has been widely implemented since its development. For example, it was included in ISO 7730:1984 [19] and ANSI/ASHRAE 55–1981 [1], as well as in many state regulations. However, its use in buildings that operate with natural ventilation or mixed mode (i.e., natural ventilation and HVAC systems) is not recommended because of its limitations [37].

Fig. 2.2 Lineal relationship between PMV and PPD, and the scales considered according to PMV

16

2 Adaptive Thermal Comfort Models for Buildings

2.3 ASHRAE 55-2017 The static thermal comfort models developed by Fanger have been widely applied. However, these thermal comfort models are based on considering fixed thermal comfort thresholds that do not depend on the oscillations of the external temperature. For this reason, the adaptive thermal comfort model emerged as a result of the research studies by Nicol and Humphreys [28] and Humphreys [15, 16], who detected that the thermal comfort models obtained in climate chambers were not adjusted to buildings that operated with natural ventilation. Users’ thermal adaptation capacity in buildings without HVAC systems was also detected when the external temperature varied, so the users’ temperature range is greater than that obtained by Fanger’s model. Studies were also developed in various countries in which the relationships of thermal comfort were compared with the types of users’ operational patterns (natural ventilation or air conditioning. Afterwards, de Dear and Brager [11] compiled 21,000 field observations and formalized the concept of adaptive thermal comfort. After the works by Dear and Brager [9, 10], a large database was created through the ASHRAE RP-884 project [8]. As a result, the first thermal comfort standard including an adaptive model was developed: ANSI/ASHRAE 55-2004 [2]. The version from 2004 was a modification of the previous ones of the standard that had already included a static thermal comfort model based on the PMV and PPD indexes. Subsequently, the standard was modified with the versions of 2013 and 2017, with the latter being the current one. Today, ASHRAE 55-2017 (2017) is the most internationally applied adaptive thermal comfort model. Although it is not technically a model with an international approach, its dataset is composed of data from countries around the world, thus being used in various countries, such as Chile [30] and Spain [4]. ASHRAE 55-2017 establishes two types of categories or limits according to the percentage of acceptability: 80 and 90%. The upper and lower limits among which the operative temperature should oscillate are established by each category Fig. 2.3. The use of these acceptability categories is not related to a certain building typology [8]. However, the conditions of indoor spaces and users are established to apply the adaptive thermal comfort model: (i) no HVAC systems in the building, (ii) metabolic rates should be between 1 and 1.3 met, and (iii) occupants could adapt their clothing between 0.5 and 1 clo. The limits are established through linear correlations with respect to an average temperature of the external air, which is known as prevailing mean outdoor air temperature (tpma(out) ). tpma(out) is a weighted average of the daily mean temperature of the previous days (Eq. 2.1). The number of days that should be considered to determine tpma(out) depends on the value given to α [5]. In this regard, ASHRAE recommends two values for α: 0.9 for climates with a high synoptic-scale weather variability (e.g., tropical climates), and 0.6 for climates with a low synoptic-scale weather variability (e.g., middle latitudes). This means that for 0.9 until 74, previous days affect the weighting of the calculation of tpma(out) , and for 0.6 the number of days is reduced to 15.

2.3 ASHRAE 55-2017

17

Fig. 2.3 Upper and lower limits of the categories of acceptability of the adaptive model of ASHRAE 55-2017

tpma(out) = (1 − α) ·

n   (i−1)   · Text,d ◦ C α

(2.1)

d =1

The determination of tpma(out) shows that the adaptive thermal comfort model of ASHRAE 55-2017 can be applied. For this purpose, values should be between 10 and 33.5 ºC. If this condition is met, the model can be applied, and the upper and lower limits of each category of acceptability can be calculated (Eqs. 2.2–2.5).   Upper limit (80%acc.) = 0.31 · tpma(out) + 21.3 ◦ C if 10 ≤ tpma(out) ≤ 33.5 (2.2)   Lower limit (80%acc.) = 0.31 · tpma(out) + 14.3 ◦ C if 10 ≤ tpma(out) ≤ 33.5 (2.3)   Upper limit (90%acc.) = 0.31 · tpma(out) + 20.3 ◦ C if 10 ≤ tpma(out) ≤ 33.5 (2.4)   Lower limit (90%acc.) = 0.31 · tpma(out) + 15.3 ◦ C if 10 ≤ tpma(out) ≤ 33.5 (2.5)

18

2 Adaptive Thermal Comfort Models for Buildings

2.4 EN 16798-1:2019 In Europe, the thermal comfort standard was introduced in EN 15251:2007 [12]. The adaptive thermal comfort model included in this standard was developed through the Smart Controls and Thermal Comfort (SCATs) project [25]. Its scheme is very similar to that of the model in ASHRAE 55 because various categories with certain thermal comfort limits are established according to a mean outdoor temperature. In particular, the EN 15251:2007 standard establishes three thermal comfort categories. Unlike the categories of acceptability of ASHRAE 55, these categories are related to certain types of buildings and users: Category I corresponds to spaces for people with low thermal adaptation (e.g., the elderly and sick people, Category II corresponds to new buildings, and Category III corresponds to existing buildings. Each category establishes the upper and lower limits among which the operative temperature should oscillate (Fig. 2.4), thus, as the category increases, the tolerance range of comfort limits is greater. The EN 15251:2007 standard establishes the same requirements established in ASHRAE for its use: (i) no HVAC systems, (ii) metabolic rates between 1 and 1.3 met, and (iii) clothing insulation between 0.5 and 1 clo. Likewise, the value of the daily mean outdoor temperature that depends on the type of limit should also be between 15 and 30 ºC for the lower limit, and between 10 and 30 ºC for the upper limit. It is worth stressing that the nomenclature for the variable of the mean outdoor temperature varies in comparison with ASHRAE by using the term running mean outdoor temperature (Trm ) (Eq. 2.6). As for the value of α, the standard recommends using a value of 0.8, supposing that temperature data from the previous 35 days could affect its calculation [5]. Consequently, the standard establishes an approach to calculate Trm by using data from the previous 7 days (Eq. 2.7). After determining the value of Trm , the upper and lower limits of each category are determined through linear correlations (Eqs. 2.8–2.13).

Fig. 2.4 Upper and lower limits of the categories of the adaptive model of EN 15251:2007 and EN 16798-1:2019

2.4 EN 16798-1:2019

19

Trm = (1 − α) ·

n   (i−1)   α · Text,d ◦ C

(2.6)

d =1

 Trm = Text,d −1 + 0.8Text,d −2 + 0.6Text,d −3 + 0.5Text,d −4 + 0.4Text,d −5 + 0.3Text,d −6    +0.2Text,d −7 /3.8 ◦ C (2.7)

  Upper limit (CategoryI ) = 0.33 · Trm + 20.8 ◦ C if 10 ≤ Trm ≤ 30

(2.8)

Lower limit (CategoryI ) = 0.33 · Trm + 16.8[◦ C] if 15 ≤ Trm ≤ 30

(2.9)

  Upper limit (CategoryII ) = 0.33 · Trm + 21.8 ◦ C if 10 ≤ Trm ≤ 30

(2.10)

  Lower limit (CategoryII ) = 0.33 · Trm + 15.8 ◦ C if 15 ≤ Trm ≤ 30

(2.11)

  Upper limit (CategoryIII ) = 0.33 · Trm + 22.8 ◦ C if 10 ≤ Trm ≤ 30

(2.12)

  Lower limit (CategoryIII ) = 0.33 · Trm + 14.8 ◦ C if 15 ≤ Trm ≤ 30.

(2.13)

The former has been recently updated by EN 16798-1:2019 [13]. This update has implied two important changes in comparison with EN 15251:2007 Fig. 2.4: (i) Trm should be between 10 and 30 ºC in the lower limit (the lower limit in EN 15251:2007 was between 15 and 30 ºC,and (ii) the equations for the lower limit are modified, whereas they are the same for the upper limit. Thus, the new thermal comfort limits of EN 16798-1:2019 are as follows:   Upper limit (CategoryI ) = 0.33 · Trm + 20.8 ◦ C if 10 ≤ Trm ≤ 30

(2.14)

  Lower limit (CategoryI ) = 0.33 · Trm + 15.8 ◦ C if 10 ≤ Trm ≤ 30

(2.15)

  Upper limit (CategoryII ) = 0.33 · Trm + 21.8 ◦ C if 10 ≤ Trm ≤ 30

(2.16)

  Lower limit (CategoryII ) = 0.33 · Trm + 14.8 ◦ C if 10 ≤ Trm ≤ 30

(2.17)

  Upper limit (CategoryIII ) = 0.33 · Trm + 22.8 ◦ C if 10 ≤ Trm ≤ 30

(2.18)

  Lower limit (CategoryIII ) = 0.33 · Trm + 13.8 ◦ C if 10 ≤ Trm ≤ 30.

(2.19)

20

2 Adaptive Thermal Comfort Models for Buildings

2.5 Other Adaptive Thermal Comfort Models ASHRAE 55-2017 and EN 16798-1:2019 are the two most used models. However, there are more adaptive thermal comfort models. Other national standards and researchers from various countries have developed specific adaptive thermal comfort models. The former includes those developed for both the Netherlands through ISSO 74 [17, 18] and China through GB/T50785 (Ministry of Housing and Urban–Rural Development (China) [27]). On the one hand, the ISSO 74 standard was first published in 2004 and modified in 2014. The adaptive thermal comfort model of these two standards presents variations, but various limits are established according to the type of building Fig. 2.5: alpha building for buildings that operate in summer with natural ventilation and allow clothing to be adapted, and (ii) beta building for buildings that operate in summer with air conditioning systems. However, ISSO 74 evolved, thanks to the approach: the version of 2004 analyzes the whole building [33, 35], and the version of 2014 analyzes the indoor spaces of the building [6]. Likewise, the database used varies:

Fig. 2.5 Upper and lower limits of the classes of the adaptive models of ISSO 74:2004 and ISSO 74:2014

2.5 Other Adaptive Thermal Comfort Models

21

data from the ASHRAE RP-884 project are used in the version of 2004 and data from the SCATs project in the version of 2014. Another main aspect are the categories of acceptability: three categories are considered in the version of 2004 (classes A, B, and C), and four categories in the version of 2014 (classes A, B, C, and D). These categories are related to the characteristics of the building or to the type of user, as in the European standard. In the version of 2014, class A corresponds to buildings in which sensitive people are, class B corresponds to new buildings, class C corresponds to existing buildings, and class D corresponds to temporary buildings. Nevertheless, the standard establishes that the upper and lower limits of classes A and B should be coincident. The values of the limits of the two versions are determined as in the other standards, using linear correlations with respect to Trm . Thus, the upper and lower limits of the version of 2004 (Eqs. 2.20–2.31) and 2014 (Eqs. 2.32–2.43) are defined. One of the characteristics of the thermal comfort limits of the classes in ISSO 74 is that they vary according to Trm Fig. 2.5. There are therefore horizontal and slope sections that vary users’ thermal expectations according to the mean outdoor temperature. Another characteristic related to the other standards is the increase made in the lower limit (the model can be applied with values of Trm up to −5 ºC), whereas the upper limit is reduced in the version of 2014 (the model could be applied with values of Trm up to 25 ºC).   Upper limit (2004, α, ClassA) = 0.11 · Trm + 22.7 ◦ C if − 5 ≤ Trm ≤ 12   Upper limit (2004, α, ClassA) = 0.31 · Trm + 20.3 ◦ C if 12 < Trm ≤ 30 (2.20)   Lower limit (2004, α, ClassA) = 0.11 · Trm + 20.2 ◦ C if − 5 ≤ Trm ≤ 30 (2.21)   Upper limit (2004, α, ClassB) = 0.11 · Trm + 23.45 ◦ C if − 5 ≤ Trm ≤ 11   Upper limit (2004, α, ClassB) = 0.31 · Trm + 21.3 ◦ C if 11 < Trm ≤ 30 (2.22)   Lower limit (2004, α, ClassB) = 0.11 · Trm + 19.45 ◦ C if − 5 ≤ Trm ≤ 30 (2.23) ◦  Upper limit (2004, α, ClassC) = 0.11 · Trm + 23.95 C if − 5 ≤ Trm ≤ 10   Upper limit (2004, α, ClassC) = 0.31 · Trm + 22 ◦ C if 10 < Trm ≤ 30 (2.24)   Lower limit (2004, α, ClassC) = 0.11 · Trm + 18.95 ◦ C if − 5 ≤ Trm ≤ 30 (2.25) ◦  Upper limit (2004, β, ClassA) = 0.11 · Trm + 22.7 C if − 5 ≤ Trm ≤ 30 (2.26)   Lower limit (2004, β, ClassA) = 0.11 · Trm + 20.2 ◦ C if − 5 ≤ Trm ≤ 30 (2.27)

22

2 Adaptive Thermal Comfort Models for Buildings

  Upper limit (2004, β, ClassB) = 0.11 · Trm + 23.45 ◦ C if − 5 ≤ Trm ≤ 30 (2.28) ◦  Lower limit (2004, β, ClassB) = 0.11 · Trm + 19.45 C if − 5 ≤ Trm ≤ 30 (2.29)   Upper limit (2004, β, ClassC) = 0.11 · Trm + 23.95 ◦ C if − 5 ≤ Trm ≤ 30 (2.30)   Lower limit (2004, β, ClassC) = 0.11 · Trm + 18.95 ◦ C if − 5 ≤ Trm ≤ 30 (2.31) ◦  Upper limit (2014, α, ClassA − B) = 24 C if − 5 ≤ Trm ≤ 10   Upper limit (2014, α, ClassA − B) = 0.33 · Trm + 20.8 ◦ C if 10 < Trm ≤ 25 (2.32)   Lower limit (2014, α, ClassA − B) = 20 ◦ C if − 5 ≤ Trm ≤ 10   Lower limit (2014, α, ClassA − B) = 0.2 · Trm + 18 ◦ C if 10 < Trm ≤ 25 (2.33) ◦  Upper limit (2014, α, ClassC) = 25 C if − 5 ≤ Trm ≤ 10   Upper limit (2014, α, ClassC) = 0.33 · Trm + 21.8 ◦ C if 10 < Trm ≤ 25 (2.34)   Lower limit (2014, α, ClassC) = 19 ◦ C if − 5 ≤ Trm ≤ 10   Lower limit (2014, α, ClassC) = 0.2 · Trm + 17 ◦ C if 10 < Trm ≤ 25

(2.35)

  Upper limit (2014, α, ClassD) = 26 ◦ C if − 5 ≤ Trm ≤ 10   Upper limit (2014, α, ClassD) = 0.33 · Trm + 22.8 ◦ C if 10 < Trm ≤ 30 (2.36)   Lower limit (2014, α, ClassD) = 18 ◦ C if − 5 ≤ Trm ≤ 10   Lower limit (2014, α, ClassD) = 0.2 · Trm + 16 ◦ C if 10 < Trm ≤ 25

(2.37)

  Upper limit (2014, β, ClassA − B) = 24 ◦ C if − 5 ≤ Trm ≤ 10   Upper limit (2014, β, ClassA − B) = 0.33 · Trm + 20.8 ◦ C if 10 < Trm ≤ 16   Upper limit (2014, β, ClassA − B) = 26 ◦ C if 16 < Trm ≤ 25 (2.38)   Lower limit (2014, β, ClassA − B) = 20 ◦ C if − 5 ≤ Trm ≤ 10   Lower limit (2014, β, ClassA − B) = 0.2 · Trm + 18 ◦ C if 10 < Trm ≤ 25 (2.39)

2.5 Other Adaptive Thermal Comfort Models

23

  Upper limit (2014, β, ClassC) = 25 ◦ C if − 5 ≤ Trm ≤ 10   Upper limit (2014, β, ClassC) = 0.33 · Trm + 21.8 ◦ C if 10 < Trm ≤ 16   Upper limit (2014, β, ClassC) = 27 ◦ C if 16 < Trm ≤ 25 (2.40)   Lower limit (2014, β, ClassC) = 19 ◦ C if − 5 ≤ Trm ≤ 10   Lower limit (2014, β, ClassC) = 0.2 · Trm + 17 ◦ C if 10 < Trm ≤ 25

(2.41)

  Upper limit (2014, β, ClassD) = 26 ◦ C if − 5 ≤ Trm ≤ 10   Upper limit (2014, β, ClassD) = 0.33 · Trm + 22.8 ◦ C if 10 < Trm ≤ 16   Upper limit (2014, β, ClassD) = 28 ◦ C if 16 ≤ Trm ≤ 25 (2.42)   Lower limit (2014, β, ClassD) = 18 ◦ C if − 5 ≤ Trm ≤ 10   Lower limit (2014, β, ClassD) = 0.2 · Trm + 16 ◦ C if 10 < Trm ≤ 25. (2.43) The Chinese thermal comfort standard (GB/T50785) was developed in 2012 for buildings with natural ventilation (Ministry of Housing and Urban–Rural Development (China) [27]). The standard establishes two different models according to the climate zone where the building is located Fig. 2.6: (i) cold climate zones,and (ii) warm and mild climate zones. Each model also establishes two different categories according to the percentage of acceptability Fig. 2.6. The form of the limits presents differences with respect to the models developed in ASHRAE 55-2017, EN 16798-1:2019, and ISSO 74:2014. Despite this, the upper and lower limits are determined through linear correlations according to the mean outdoor temperature (Eqs. 2.44–2.51).

Fig. 2.6 Upper and lower limits of the classes of the adaptive models of GB/T50785

24

2 Adaptive Thermal Comfort Models for Buildings

  Upper limit (Cold − CategoryI ) = 0.77 · Trm + 12.04 ◦ C if 7.7 ≤ Trm ≤ 20.7 (2.44)   Lower limit (Cold − CategoryI ) = 0.87 · Trm + 2.76 ◦ C if 17.5 ≤ Trm ≤ 29 (2.45) ◦  Upper limit (Cold − CategoryII ) = 0.73 · Trm + 15.28 C if 3.7 ≤ Trm ≤ 20.2 (2.46)   Lower limit (Cold − CategoryII ) = 0.91 · Trm − 0.48 ◦ C if 18.1 ≤ Trm ≤ 31.3 (2.47)   Upper limit (W arm − CategoryI ) = 0.77 · Trm + 9.34 ◦ C if 11.2 ≤ Trm ≤ 24.2 (2.48) ◦  Lower limit (W arm − CategoryI ) = 0.87 · Trm − 0.31 C if 21 ≤ Trm ≤ 32.5 (2.49)   Upper limit (W arm − CategoryII ) = 0.73 · Trm + 12.72 ◦ C if 7.2 ≤ Trm ≤ 23.7 (2.50)   Lower limit (W arm − CategoryII ) = 0.91 · Trm − 3.69 ◦ C if 21.6 ≤ Trm ≤ 34.8. (2.51) Thus, the characteristics of the standards designed for the Netherlands and China have provided them with specific models. However, these adaptive thermal comfort models included in standards are not the only ones. Many specific models designed for each country or even for a region could be found in the scientific literature. An example is China itself because many studies have developed specific models for certain regions. The first study was conducted by Yang et al. [36]. In this work, field studies were performed in the dry–hot and dry–cold areas of Turpan Basin (China). The model presented a structure based on two categories of acceptability Fig. 2.7 and with limits based on simple linear correlations, similar to ASHRAE 55-2017 (Eqs. 2.52–2.55).   Upper limit (80%acc.) = 0.30 · Trm + 25.9 ◦ C if − 7 ≤ tpma(out) ≤ 30 (2.52)   Lower limit (80%acc.) = 0.32 · Trm + 14.88 ◦ C if − 7 ≤ tpma(out) ≤ 30 (2.53)   Upper limit (90%acc.) = 0.30 · Trm + 23.6 ◦ C if − 7 ≤ tpma(out) ≤ 30 (2.54)   Lower limit (90%acc.) = 0.31 · Trm + 17.14 ◦ C if − 7 ≤ tpma(out) ≤ 30. (2.55)

2.5 Other Adaptive Thermal Comfort Models

25

Fig. 2.7 Upper and lower limits of the categories of acceptability designed by Yang et al. [36] to be applied in areas of Turpan Basin (China)

Another study conducted in China was by Jiao et al. [20]. This study developed adaptive thermal comfort models for the elderly living in Shanghai (China) due to the existing limitations in the adaptive thermal comfort standards for this group (ASHRAE 55-2017, EN 15251:2007, and GB/T50785). For this purpose, a total of 1,040 surveys were performed in various buildings. The results obtained various adaptive thermal comfort models according to the season of the year Fig. 2.8. In each model by season, only upper and lower limits were established, which were obtained through linear correlations with respect to the mean outdoor temperature (Eqs. 2.56–2.61).   Upper limit (W inter) = 0.706 · Trm + 11.975 ◦ C if 3.1 ≤ Trm ≤ 10

(2.56)

  Lower limit (W inter) = 0.706 · Trm + 6.775 ◦ C if 3.1 ≤ Trm ≤ 10

(2.57)

  Upper limit (Spring/autumn) = 0.84 · Trm + 12.535 ◦ C if 10 ≤ Trm ≤ 20.8 (2.58)   Lower limit (Spring/autumn) = 0.84 · Trm + 1.335 ◦ C if 10 ≤ Trm ≤ 20.8 (2.59) ◦  Upper limit (Summer) = 0.418 · Trm + 17.56 C if 22 ≤ Trm ≤ 29 (2.60)   Lower limit (Summer) = 0.418 · Trm + 14.36 ◦ C if 22 ≤ Trm ≤ 29.

(2.61)

26

2 Adaptive Thermal Comfort Models for Buildings

Fig. 2.8 Upper and lower limits of the categories of acceptability designed by Jiao et al. [20] for the elderly living in Shanghai (China)

However, the local studies recently carried out are not just located in China, but in other countries including Australia, Chile, India, and Romania as well. The first was by Manu et al. [24], who developed an adaptive thermal comfort model for office buildings in India. The reason was the lack of adaptation of the international models to the buildings in India. After compiling 6,330 surveys, two different models were developed according to the type of operational pattern in the building Fig. 2.9: with natural ventilation and with mixed mode. Each model also established three different categories according to the percentage of acceptability: 80%, 85%, and 90%. The upper and lower limits were determined through linear correlations according to the mean outdoor temperature (Eqs. 2.62–2.73). Moreover, the operational pattern varied in the possibility of applying the adaptive approach because the range of values among which Trm should oscillate changed: in the building with natural ventilation, Trm should oscillate between 12.5 and 31 ºC, and in the mixed-mode building, Trm should oscillate between 13 and 38.5 ºC.

2.5 Other Adaptive Thermal Comfort Models

27

Fig. 2.9 Upper and lower limits of the categories of acceptability designed by Manu et al. [24] for office buildings in India

  Upper limit (N V B, 80%acc.) = 0.54 · Trm + 16.93 ◦ C if 12.5 ≤ Trm ≤ 31 (2.62)   Lower limit (N V B, 80%acc.) = 0.54 · Trm + 8.73 ◦ C if 12.5 ≤ Trm ≤ 31 (2.63)   Upper limit (N V B, 85%acc.) = 0.54 · Trm + 16.13 ◦ C if 12.5 ≤ Trm ≤ 31 (2.64) ◦  Lower limit (N V B, 85%acc.) = 0.54 · Trm + 9.53 C if 12.5 ≤ Trm ≤ 31 (2.65)   Upper limit (N V B, 90%acc.) = 0.54 · Trm + 15.23 ◦ C if 12.5 ≤ Trm ≤ 31 (2.66) ◦  Lower limit (N V B, 90%acc.) = 0.54 · Trm + 10.53 C if 12.5 ≤ Trm ≤ 31 (2.67)   Upper limit (MM , 80%acc.) = 0.28 · Trm + 23.77 ◦ C if 13 ≤ Trm ≤ 38.5 (2.68)   Lower limit (MM , 80%acc.) = 0.28 · Trm + 11.97 ◦ C if 13 ≤ Trm ≤ 38.5 (2.69) ◦  Upper limit (MM , 85%acc.) = 0.28 · Trm + 22.67 C if 13 ≤ Trm ≤ 38.5 (2.70)   Lower limit (MM , 85%acc.) = 0.28 · Trm + 13.07 ◦ C if 13 ≤ Trm ≤ 38.5 (2.71)

28

2 Adaptive Thermal Comfort Models for Buildings

  Upper limit (MM , 90%acc.) = 0.28 · Trm + 21.37 ◦ C if 13 ≤ Trm ≤ 38.5 (2.72) ◦  Lower limit (MM , 90%acc.) = 0.28 · Trm + 14.37 C if 13 ≤ Trm ≤ 38.5. (2.73) Another important study was conducted by Udrea et al. [32], which focused on buildings that operate with natural ventilation in Bucharest (Romania). This study is based on field studies conducted between 2013 and 2014 in Bucharest. Data were analyzed with the methodology of ASHRAE 55, so the structure of the adaptive thermal comfort model by Udrea et al. [32] was very similar to that of ASHRAE 55-2017 Fig. 2.10. This implies that the values among which the mean outdoor temperature should oscillate to apply the model should be equal to those of ASHRAE 55-2017: between 10 and 33.5 ºC. Nonetheless, the equation related to each limit was different from that of ASHRAE 55-2017 (Eqs. 2.74–2.77), so the model designed for Bucharest was more adjusted to the thermal expectations of users living in this region.   Upper limit (80%acc.) = 0.25 · Trm + 22.59 ◦ C if 10 ≤ Trm ≤ 33.5

(2.74)

  Lower limit (80%acc.) = 0.25 · Trm + 16.81 ◦ C if 10 ≤ Trm ≤ 33.5

(2.75)

  Upper limit (90%acc.) = 0.25 · Trm + 21.4 ◦ C if 10 ≤ Trm ≤ 33.5

(2.76)

  Lower limit (90%acc.) = 0.25 · Trm + 18 ◦ C if 10 ≤ Trm ≤ 33.5.

(2.77)

Fig. 2.10 Upper and lower limits of the categories of acceptability designed by Udrea et al. [32] for buildings naturally ventilated in Bucharest (Romania)

2.5 Other Adaptive Thermal Comfort Models

29

Fig. 2.11 Upper and lower limits of the categories of acceptability designed by Pérez-Fargallo et al. [29] for social dwellings in Concepción (Chile)

A similar study was conducted by Pérez-Fargallo et al. [29], who addressed the limitations presented by the international adaptive thermal comfort models (ASHRAE 55-2017 and EN 16798-1:2019) in view of the greatest thermal adaptation presented by residents in social dwellings in Chile when temperatures are low. Based on 709 surveys conducted in Concepción (Chile), a new adaptive thermal comfort model was defined for Chilean social dwellings, considering the greatest adaptation at low temperatures Fig. 2.11. Two categories were established according to the percentage of acceptability: 80% and 90%. Each category modified the upper and lower limits in the range of Trm between 5 and 6.5 ºC (Eqs. 2.78–2.80) because of the lowest tolerance presented at low temperatures.   Upper limit (80%acc.) = 0.115 · Trm + 21.17 ◦ C if 5 ≤ Trm ≤ 6.5   Upper limit (80%acc.) = 0.115 · Trm + 21.17 ◦ C if 5 ≤ Trm ≤ 6.5   Upper limit (80%acc.) = 0.678 · Trm + 17.51 ◦ C if 6.5 < Trm ≤ 12

(2.78)

  Lower limit (80%acc.) = 0.115 · Trm + 13.17 ◦ C if 5 ≤ Trm ≤ 6.5   Lower limit (80%acc.) = 0.678 · Trm + 9.51 ◦ C if 6.5 < Trm ≤ 33.5

(2.79)

  Upper limit (90%acc.) = 0.115 · Trm + 19.67 ◦ C if 5 ≤ Trm ≤ 6.5   Upper limit (90%acc.) = 0.678 · Trm + 16.01 ◦ C if 6.5 < Trm ≤ 12

(2.80)

  Lower limit (90%acc.) = 0.115 · Trm + 14.67 ◦ C if 5 ≤ Trm ≤ 6.5   Lower limit (90%acc.) = 0.678 · Trm + 11.01 ◦ C if 6.5 < Trm ≤ 33.5. (2.81)

30

2 Adaptive Thermal Comfort Models for Buildings

Fig. 2.12 Upper and lower limits of the categories of acceptability designed by Williamson and Daniel [34] for residential buildings located in mild climates in Australia

Finally, a study by Williamson and Daniel [34] developed an adaptive thermal comfort model for residential buildings located in mild climates in Australia. The database was composed of more than 50,000 observations, so the possibilities of users’ adaptation in the residential buildings in this region were widely known. After the data analysis, an adaptive thermal comfort model with two different categories was defined (Fig. 2.12): Category I (corresponding to 80% under adequate conditions) and Category II (corresponding to 90% under adequate conditions). It is worth stressing that the concept of adequate conditions was different from that of acceptability of ASHRAE 55-2017. Linear correlations were established for each category to determine their lower and upper limits (Eqs. 2.82–2.85). It was established that to apply this model, the mean outdoor temperature should oscillate between 5 and 33.5 ºC.   Upper limit (CategoryI ) = 0.26 · tpma(out) + 19.4 ◦ C if 5 ≤ tpma(out) ≤ 33.5 (2.82)   Lower limit (CategoryI ) = 0.26 · tpma(out) + 12.4 ◦ C if 5 ≤ tpma(out) ≤ 33.5 (2.83)   Upper limit (CategoryII ) = 0.26 · tpma(out) + 20.4 ◦ C if 5 ≤ tpma(out) ≤ 33.5 (2.84)   Lower limit (CategoryII ) = 0.26 · tpma(out) + 11.4 ◦ C if 5 ≤ tpma(out) ≤ 33.5. (2.85)

2.6 Conclusions

31

2.6 Conclusions Thermal comfort could be a more complex issue than it may seem. The two main approaches (static and adaptive) and the various developments presented by standards and research studies show that thermal comfort conditions could vary according to the type of building and user, location, and climate. Moreover, the existing operational pattern could vary the oscillations presented by the internal operative temperature. Thus, the research related to thermal comfort (and more particularly, adaptive thermal comfort) is continuously updated, as shown by both the modifications of the international standards and the recent studies published over the last 5 years. Nonetheless, adaptive models show that users living in buildings with natural ventilation have a greater adaptation to temperature variations, increasing the values of upper and lower limits. This could mean that a larger number of thermal comfort hours is maintained, unlike users with static operational patterns who required the use of HVAC systems in a shorter period. Consequently, many research studies have presented the potential of adaptive thermal comfort models as ECM in buildings.

References 1. American National Standards Institute/American Society of Heating Refrigerating and AirConditioning Engineers (ANSI/ASHRAE) (1981) ANSI/Standard 55–1981 thermal environmental conditions for human occupancy 2. American National Standards Institute/American Society of Heating Refrigerating and AirConditioning Engineers (ANSI/ASHRAE) (2004) ANSI/Standard 55-2004 thermal environmental conditions for human occupancy 3. American Society of Heating Refrigerating and Air Conditioning Engineers (ASHRAE) (2017) ASHRAE Standard 55-2017 Thermal environmental conditions for human occupancy. Atlanta, GA, United States 4. Barbadilla-Martín E, Salmerón Lissén JM, Guadix Martín J et al (2017) Field study on adaptive thermal comfort in mixed mode office buildings in southwestern area of Spain. Build Environ 123:163–175. https://doi.org/10.1016/j.buildenv.2017.06.042 5. Bienvenido-Huertas D, Sánchez-García D, Pérez-Fargallo A, Rubio-Bellido C (2020) Optimization of energy saving with adaptive setpoint temperatures by calculating the prevailing mean outdoor air temperature. Build Environ 170. https://doi.org/10.1016/j.buildenv.2019. 106612 6. Boerstra AC, van Hoof J, van Weele AM (2015) A new hybrid thermal comfort guideline for the Netherlands: background and development. Archit Sci Rev 58:24–34. https://doi.org/10. 1080/00038628.2014.971702 7. Braubach M (2013) Ferrand A (2013) Energy efficiency, housing, equity and health. Int J Public Heal 58:331–332. https://doi.org/10.1007/s00038-012-0441-2 8. Carlucci S, Bai L, de Dear R, Yang L (2018) Review of adaptive thermal comfort models in built environmental regulatory documents. Build Environ 137:73–89. https://doi.org/10.1016/ j.buildenv.2018.03.053 9. de Dear R, Brager GS (2002) Thermal comfort in naturally ventilated buildings: revision to ASHRAE standards 55. J Energy Build 34:549–561. https://doi.org/10.1016/S0378-778 8(02)00005-1

32

2 Adaptive Thermal Comfort Models for Buildings

10. de Dear R, Brager GS (2001) The adaptive model of thermal comfort and energy conservation in the built environment. Int J Biometeorol 45:100–108. https://doi.org/10.1007/s00484010 0093 11. de Dear R, Brager GS (1998) Developing an adaptive model of thermal comfort and preference. Center for the Built Environment, UC Berkeley. https://escholarship.org/uc/item/4qq2p9c6 12. European Committee for Standardization (2007) EN 15251:2007 Indoor environmental input parameters for design and assessment of energy performance of buildings addressing indoor quality, thermal environment, lighting and acoustics. European Committee for Standardization, Brussels 13. European Committee for Standardization (2019) EN 16798-1:2019 Energy performance of buildings-Ventilation for buildings-Part 1: Indoor environmental input parameters for design and assessment of energy performance of buildings addressing indoor air quality, thermal environment, lighting and acous 14. Fanger PO (1970) Thermal comfort: analysis and applications in environmental engineering 15. Humphreys M (1975) Field studies in thermal comfort compared and applied 16. Humphreys M (1978) Outdoor temperatures and comfort indoors. Build Res Pract 6:92. https:// doi.org/10.1080/09613217808550656 17. Instituut Voor Studie En Stimulering Van Onderzoek (2004) ISSO-publicatie 74 Thermische behaaglijkheid. Rotterdam, Netherlands 18. Instituut Voor Studie En Stimulering Van Onderzoek (2014) ISSO-publicatie 74 Thermische behaaglijkheid 19. International Organization for Standardization (1984) ISO 7730:1984-Ergonomics of the thermal environment-Analytical determination and interpretation of thermal comfort using calculation of the PMV and PPD indices and local thermal comfort criteria 20. Jiao Y, Yu H, Yu Y et al (2020) Adaptive thermal comfort models for homes for older people in Shanghai, China. Energy Build 215:109918. https://doi.org/10.1016/j.enbuild.2020.109918 21. Karyono K, Abdullah BM, Cotgrave AJ, Bras A (2020) The adaptive thermal comfort review from the 1920s, the present, and the future. Dev Built Environ 106192. https://doi.org/10.1016/ j.dibe.2020.100032 22. Liddell C, Guiney C (2015) Living in a cold and damp home: frameworks for understanding impacts on mental well-being. Public Health 129:191–199. https://doi.org/10.1016/j.puhe. 2014.11.007 23. Liddell C, Morris C, Thomson H, Guiney C (2016) Excess winter deaths in 30 European countries 1980–2013: a critical review of methods. J Public Health (Bangkok) 38:806–814. https://doi.org/10.1093/pubmed/fdv184 24. Manu S, Shukla Y, Rawal R et al (2016) Field studies of thermal comfort across multiple climate zones for the subcontinent: India Model for Adaptive Comfort (IMAC). Build Environ 98:55–70. https://doi.org/10.1016/J.BUILDENV.2015.12.019 25. McCartney KJ, Nicol JF (2002) Developing an adaptive control algorithm for Europe. Energy Build 34:623–635. https://doi.org/10.1016/S0378-7788(02)00013-0 26. Middlemiss L, Gillard R (2015) Fuel poverty from the bottom-up: characterising household energy vulnerability through the lived experience of the fuel poor. Energy Res Soc Sci 6:146– 154. https://doi.org/10.1016/j.erss.2015.02.001 27. Ministry of Housing and Urban-Rural Development (China) (2012) (GB/T 50785–2012) Evaluation standard for indoor thermal environment in civil buildings. Standardization Administration of China Beijing, China 28. Nicol JF, Humphreys MA (1973) Thermal comfort as part of a self-regulating system. Build Res Pract 1:174–179. https://doi.org/10.1080/09613217308550237 29. Pérez-Fargallo A, Pulido-Arcas J, Rubio-Bellido C et al (2018) Development of a new adaptive comfort model for low income housing in the central-south of Chile. Energy Build. https://doi. org/10.1016/J.ENBUILD.2018.08.030 30. Rubio-Bellido C, Pérez-Fargallo A, Pulido-Arcas JA, Trebilcock M (2017) Application of adaptive comfort behaviors in Chilean social housing standards under the influence of climate change. Build Simul 10:933–947. https://doi.org/10.1007/s12273-017-0385-9

References

33

31. Thomson H, Snell C (2013) Quantifying the prevalence of fuel poverty across the European Union. Energy Policy 52:563–572. https://doi.org/10.1016/j.enpol.2012.10.009 32. Udrea I, Croitoru C, Nastase I et al (2018) First adaptive thermal comfort equation for naturally ventilated buildings in Bucharest, Romania. Int J Vent 17:149–165. https://doi.org/10.1080/147 33315.2017.1356057 33. Van Der Linden AC, Boerstra AC, Raue AK et al (2006) Adaptive temperature limits: a new guideline in the Netherlands: a new approach for the assessment of building performance with respect to thermal indoor climate. Energy Build 38:8–17. https://doi.org/10.1016/j.enbuild. 2005.02.008 34. Williamson T, Daniel L (2020) A new adaptive thermal comfort model for homes in temperate climates of Australia. Energy Build 210:109728. https://doi.org/10.1016/j.enbuild. 2019.109728 35. Yang J, Linden van der KAC, Kurvers SSR et al (2007) Indoor climate guidelines in The Netherlands. Constr Innov. https://doi.org/10.1108/14714170710721304 36. Yang L, Fu R, He W, et al (2020) Adaptive thermal comfort and climate responsive building design strategies in dry–hot and dry–cold areas: Case study in Turpan, China. Energy Build 209. https://doi.org/10.1016/j.enbuild.2019.109678 37. Yang X, Zhong K, Kang Y, Tao T (2015) Numerical investigation on the airflow characteristics and thermal comfort in buoyancy-driven natural ventilation rooms. Energy Build 109:255–266. https://doi.org/10.1016/j.enbuild.2015.09.071

Chapter 3

Application of Adaptive Thermal Comfort Models for Energy Saving in Buildings

Abstract The use of natural ventilation achieves considerable energy consumption savings and reduces overheating risk in summer; however, it is less effective in regions where heating energy is more demanding. In this sense, the use of adaptive thermal comfort models is an opportunity to use natural ventilation optimally coupled with air conditioning systems when necessary. In this chapter, the use of variable setpoint temperatures has been analyzed, for static and adaptive patterns. Results show energy savings using two approaches with low economic investment and without comprising users’ thermal comfort. This chapter also analyzes the barriers and opportunities to improve energy performance in extant buildings. Results set that the potential of the application of the adaptive strategies on the Earth’s surface is high although it depends on the climatic conditions. Moreover, the applicability could be modified by global warming, considering future climate scenarios, but it still maintains significant energy savings. For that reason, architects and engineers are crucial to identify and apply the most appropriate adaptive measures in specific cases. Keywords Thermal comfort · Adaptive comfort · Energy saving · Payback periods · Energy conservation measures · Climate change · Natural ventilation · Climate adaption

3.1 Introduction The main source of energy consumption in residential buildings is mainly the use of HVAC systems [23, 48]. The application of ECMs to reduce the HVAC system energy consumption would therefore allow for the proposed sustainability goals to be achieved. However, both the low effectiveness of the ECMs to achieve energy savings in the countries in the south of Europe and the possible rebound effects reflect the importance of the users’ role (i.e., operational patterns). In this regard, adaptive models could be an opportunity to achieve energy savings in buildings in combination with other ECMs or independently. Adaptive models could be applied in two ways [4]: through adaptive natural ventilation and by using adaptive setpoint temperatures. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 D. Bienvenido-Huertas and C. Rubio-Bellido, Adaptive Thermal Comfort of Indoor Environment for Residential Buildings, SpringerBriefs in Architectural Design and Technology, https://doi.org/10.1007/978-981-16-0906-0_3

35

36

3 Application of Adaptive Thermal Comfort Models …

3.2 Adaptive Natural Ventilation Natural ventilation is an effective strategy to compensate thermal loads in summer. With this technique, the indoor space is cooled by using the external air (which is colder than that inside the room), thus reducing the energy consumption of air conditioning systems [14]. Energy consumption is reduced without any economic cost [21, 25]. However, the bioclimatic context of the building should be analyzed to assess the effectiveness of natural ventilation through factors such as climate [4], the regularity of thermal breezes [17], and the effects among buildings [27]. Moreover, constructive factors such as the thermal properties of the envelope [12, 13] and the height of the building [44] should also be considered. Nonetheless, the bioclimatic component plays an essential role in the effectiveness of natural ventilation. In this regard, natural ventilation is less effective in regions where heating energy is more demanded [22]. This duality of the effectiveness of natural ventilation between heating and cooling consumption was reported by Santos and Leal [35]. This work showed that the use of natural ventilation in Helsinki, Paris, and Lisbon reduces the energy consumption of the air conditioning, whereas heating consumption significantly increases. Thus, this strategy should only be used in seasons when the cooling energy demand is greater (e.g., in summer). The use of natural ventilation achieves significant energy consumption savings in summer, as some studies have shown: • Santamouris et al. [34] determined that the application of natural ventilation in dwellings during the night could save the cooling energy demand by 12 kWh/m2 . • Tong et al. [45] saved the cooling energy consumption in office buildings by 78% by using natural ventilation. • A similar study was conducted by Gil-Báez et al. [19], in which natural ventilation was applied in schools. The results showed a saving in energy consumption of up to 33%. In addition, this technique could reduce the overheating risk due to climate change [20], although natural ventilation should be combined with air conditioning systems (i.e., buildings that operate with natural ventilation and air conditioning systems [40]). However, appropriate guidelines should be established for natural ventilation because opening windows without any control could generate thermal discomfort situations [38]. Thus, the use of adaptive thermal comfort models is an opportunity to use natural ventilation optimally. For this purpose, the approach defined by SánchezGarcía et al. [31] could be used. In this work, natural ventilation was used when the external temperature was between the upper and lower limits defined by the categories of the adaptive thermal comfort model used (e.g., categories I, II, and III of EN 16798-1:2019 or the percentages of acceptabilities (80% and 90%) of ASHRAE 55-2017). Figure 3.1 shows a scheme of the process of adaptive natural ventilation. If the external temperature and the internal operative temperature are not within the thermal comfort limits, the air conditioning systems are used. The use of these natural ventilation approaches, together with air conditioning systems in the

3.2 Adaptive Natural Ventilation

37

Fig. 3.1 Scheme of the approach of adaptive natural ventilation

hours of exceeding the upper limit, ensures that there are no thermal discomfort hours. Moreover, the conditions of natural ventilation vary according to the oscillations of climate in the previous days, so the window opening pattern is adapted according to the changes produced in the climate. The use of this approach has achieved very satisfactory results. Sánchez-García et al. [31] achieved a saving in the energy demand of up to 74.6% in an office building located in Seville. Bienvenido-Huertas et al. [7] analyzed the application of adaptive natural ventilation both to achieve energy savings in social dwellings and to reduce the EP impact. The results showed that the use of this technique in coastal zones could achieve energy savings of almost 100% in most assumptions.

3.3 Adaptive Setpoint Temperatures One of the possibilities to reduce energy consumption is by modifying the setpoint temperatures [31, 32] as they influence the operation of HVAC systems [29]. The use of setpoint temperatures that are appropriate to the environmental characteristics of each zone would reduce building energy consumption without making a high economic investment [47]. In addition, more sustainable operational patterns of HVAC systems would decrease the payback periods of the ECMs implemented [8]. Many studies have analyzed the influence of the variation of the setpoint temperatures in the building energy saving based on Fanger’s method (i.e., using fixed setpoint temperatures during the whole season):

38

3 Application of Adaptive Thermal Comfort Models …

• Parry et al. [26] studied strategies to reduce the energy consumption of an office building in Zurich. The increase of the cooling setpoint temperature between 2 and 4 °C reduced the annual energy consumption by a third. • Wan et al. [49] analyzed the possibilities to reduce the energy consumption of an office building in Hong Kong. The use of cooling setpoint temperatures greater than 25 °C allowed high energy savings to be achieved both in the current climate scenario and in climate change scenarios predicted for the twenty-first century. • Spyropoulos and Balaras [41] analyzed several office spaces in Greece. The use of setpoint temperatures of 20 °C for heating and 26 °C for cooling reduced almost 50% of the total energy consumption. These research studies therefore obtained acceptable results by varying setpoint temperatures following static patterns; however, the potential of people’s thermal adaptation to external climate variations was not shown. The thermal conditioning of internal spaces by using adaptive setpoint temperatures consists of adapting the value of the setpoint temperature to the limit value obtained each day: the lower limit value is useful for the heating adaptive setpoint temperature and the upper limit value for the cooling adaptive setpoint temperature. If the prevailing mean outdoor air temperature is greater than 33.5 °C or lower than 10 °C, a fixed value for the setpoint temperatures should be used corresponding to the extension of the limits of the adaptive model (Fig. 3.2) [30]. In recent years, many studies have analyzed the energy saving obtained with the variation of setpoint temperatures by adapting them to adaptive thermal comfort models (Table 3.1). Given the great variety of approaches analyzed, these studies

Fig. 3.2 Scheme of the approach of the adaptive setpoint temperatures

3.3 Adaptive Setpoint Temperatures

39

Table 3.1 Summary of the energy-saving results obtained by applying adaptive setpoint temperatures References

Strategy and description

Energy saving obtained

Yun et al. [51]

Analysis of the application of an Energy saving of 22% in energy adaptive thermal comfort model consumption to use HVAC systems in office buildings in South Korea

Sánchez-Guevara Sánchez et al. [33]

Analysis of the application of monthly adaptive setpoint temperatures in 3 residential buildings in Avila, Madrid, and Seville (Spain)

Energy saving between 20 and 80% in energy consumption

Sánchez-García et al. [31]

Analysis of adaptive setpoint temperatures in office buildings in Seville (Spain)

Energy saving between 36.7 and 59.5% in energy consumption

Sánchez-García et al. [30]

Analysis of adaptive setpoint temperatures in a residential building with respect to the residential profile of the Spanish Building Technical Code in Avila, Madrid, and Seville (Spain)

Energy saving between 10 and 46% in building energy consumption with respect to the operational profile from the Spanish Building Technical Code

Bienvenido-Huertas et al. [9]

Analysis of the application of adaptive setpoint temperatures in an office building located in the main cities of the Iberian Peninsula

Energy saving obtained by the adaptive strategies in the current and future scenarios (2050 and 2100), and a greater saving obtained with EN 16798-1:2019 than with adaptive thermal comfort models

Bienvenido-Huertas et al. [6]

Analysis of the optimal weight required to determine the adaptive setpoint temperatures in the energy consumption of a residential building in Avila, Madrid, and Seville

Energy saving between 25.73 and 44.89% in the total energy consumption

used other standards of adaptive thermal comfort models, such as EN 15251:2007 [16] or ASHRAE 55-2017 [1]. The results showed that the application of these adaptive thermal comfort models considerably reduces building energy consumption by varying the operational patterns of HVAC systems, so a high economic investment is not required. In addition, energy consumption is reduced without influencing the users’ thermal comfort.

40

3 Application of Adaptive Thermal Comfort Models …

3.4 Barriers and Opportunities Energy efficiency projects are mainly based on the use of ECMs to improve the energy performance of the existing buildings. However, these improvements are associated with a series of barriers that can make their correct implementation a challenge. After reviewing exhaustively the scientific literature [2, 10, 11, 15, 18, 24, 28, 36, 37, 39, 46, 50, 52], barriers could be divided into three groups (Fig. 3.3): economic, technical, and institutional support. Economic barriers are related to the high costs of ECMs. The possible high economic investment for users to improve the envelope of the building or HVAC systems could imply that the payback periods are long and that users do not actually obtain the economic recovery of the investment. Thus, there is not an improvement in the form of a lower electricity bill, leading to not investing in expensive ECMs. Moreover, maintenance costs related to new installations could make the implementation of these ECMs more difficult due to the increase in the initial investment of the ECMs. There is therefore a greater tendency to adopt ECMs that do not require expensive maintenance tasks (e.g., the improvement of the envelope), instead of implementing installations with subsequent maintenance costs. At a technological level, the main barriers are associated, on the one hand, with the existing incompatibility of the emergent energy technologies with their possibility of implementation in the existing building stock, and on the other hand, with the technical training and experience of workers from construction companies with regards to energy efficiency. The first limitation is of great importance due to the need for establishing effective measures to improve the energy performance of the existing buildings; this energy is usually quite deficient because most buildings have been built before the development of the energy efficiency standards of each country. Given that building restoration should be the first option (instead of demolition and new construction), adequate ECMs should be implemented in existing buildings, including the possibility of implementing ECMs for buildings with different geometric, thermophysical, and location characteristics that, in cases such as historic centers, could be characterized by not having energy self-production systems, so the ECMs that can be adopted are only used to improve the passive behavior of the building and to replace HVAC systems.

Fig. 3.3 Barrier dimensions for building energy improvement

3.4 Barriers and Opportunities

41

Finally, the last typology of barriers is related to the institutional support on the part of state and regional governments. This support can be divided into three measures: the economic financing of a percentage of the investment of ECMs, the implementation of energy efficiency standards, and the regulation of energy prices. In Spain, the government is strongly involved in these issues. Various standards with regards to building energy efficiency have emerged in the country over the last 40 years, from the NBE-CT-79 to the Spanish Building Technical Code [42] (CTE in Spanish) in 2006 and its subsequent reviews in the following years until 2020. Regarding economic financing, there are programmes such as the Aid Programme for the Energy Rehabilitation of Buildings (PAREER programme), in effect until 2018, or the Development Programme to improve the energy efficiency and sustainability of dwellings, from the 2018–2021 State Housing Plan that constitutes a framework to finance the ECMs that improve the energy performance of existing buildings. Finally, there is a contracting modality in the electricity bill in which the prices related to the energy term are regulated and published by the government (i.e., voluntary price for the small consumer [43] (PVPC in Spanish)), so users can control the most appropriate hours to use lighting equipment and systems. Therefore, the main barriers are related to economic and technical dimensions. The adoption of appropriate strategies for the economic profitability of investments for the energy improvement of existing buildings would ensure an adequate and progressive transition to a low-carbon economy. For this purpose, the ECMs with greater facilities to be implemented in the existing buildings (e.g., improvement of the envelope or replacement of HVAC systems) could have short payback periods [8], thus facilitating building energy improvement without implying a great effort for resident families and without being exposed to the possible variations of cofinancing with public bodies. Adaptive energy-saving strategies could therefore be an opportunity to achieve energy saving in buildings, both new and existing, thus always ensuring the users’ thermal comfort. The use of effective strategies based on the use of both external temperature probes and algorithms installed in the thermostats to self-configure adaptive setpoint temperatures would ensure the possibility of using adaptive setpoint temperatures without a commitment and an exhaustive control on the part of the users of which setpoint temperature should be used [5]. However, the possibility of using these models should be ensured, so adaptive setpoint temperatures or natural ventilation are an appropriate energy-saving strategy. As described above, the use of adaptive thermal comfort models depends on the average value of the daily mean outdoor temperature of the previous days and, according to the values obtained, the temperature range in which the internal operative temperature should oscillate is determined to guarantee users’ thermal comfort. If this daily mean outdoor temperature is very low or high, the adaptive thermal comfort model is not applied on that day (i.e., the user could not adapt to these temperatures and should adopt the behavior that would be used in the limit value of the adaptive model). Therefore, the possibility of application and the effectiveness of adaptive setpoint temperatures directly depend on the climate conditions of the building. To analyze this aspect, the potential of applying adaptive thermal comfort

42

3 Application of Adaptive Thermal Comfort Models …

models has been studied all over the world according to the climate [4]. The analysis was performed by obtaining hourly climate data through Meteonorm in 15,897 locations distributed on the Earth’s surface. These climate data were obtained both in the current and A2 scenarios in 2050 to assess the climate change effects. The percentage of application of the days of the year was determined by assessing the days of the year when the running mean outdoor temperature was within the range of values established. Likewise, the percentage of hours was obtained by determining the sum of annual hours when the external temperature was within the limits of adaptive thermal comfort. Finally, heating and cooling degrees were saved by comparing static setpoint temperatures with adaptive setpoint temperatures. A total of three for heating (20, 21, and 22 °C) and three for cooling (25, 26, and 27 °C) were selected to configure the static setpoint temperatures. The configurations of the adaptive temperatures were obtained from the limits of the adaptive thermal comfort model: the heating temperature was obtained from the lower limit and the cooling temperature from the upper limit. The results showed that the potential of application of the adaptive strategies on the Earth’s surface is high (Fig. 3.4.). Thus, most of the Earth’s surface presents a percentage of application greater than 50% of the days of the year. This relationship at a surface level becomes more important in relation to demographic aspects because 56.26% of the world population lives in zones with an application between 90 and 100% of the days of the year. In addition, there is a relationship between latitude-altitude and the potential of application of the adaptive models, so latitudes close to the equator have a high potential of application. However, there are other Earth’s zones where the values of percentages of application are high. This could be the case in the countries in the south of Europe. As an example, Fig. 3.5 shows the percentage of days of the year when

Fig. 3.4 Percentage of days of the year when the adaptive model can be applied (current scenario)

3.4 Barriers and Opportunities

43

Fig. 3.5 Percentage of the days of the year when the adaptive model can be applied in the south of Spain (current scenario)

the adaptive model could be applied in the south of Spain. Most territories present an application of the adaptive thermal comfort models greater than 70% of the days of the year, and certain regions, such as the Baetic System, present lower percentages of application. However, a percentage of the application greater than 50% was found in all municipalities. Likewise, it is worth stressing the great application of adaptive thermal comfort models in coastal municipalities, with percentages of application between 90 and 100% of the days of the year. These results have shown an ascending tendency in the region from the middle of the twentieth century to date, [3], thus reflecting the influence of the evolution of climate on the application of adaptive models. The evolution of climate will slightly vary the possibility of application of adaptive thermal comfort models (Fig. 3.6). Compared with the results obtained in the current scenario (Fig. 3.7), there was a slight increase of between 0.05 and 1.32% in the percentages of the days of the year between 50 and 90%. Likewise, the percentage range slightly varied between 90 and 100% of the days of the year. This is a direct consequence of the increase in the external temperature that will generate two effects: (i) an increase in the possibility of application in the coldest zones in the current scenario, and (ii) an overcoming of the upper threshold of the mean outdoor temperature that will generate that the warmest zones today reduce the possibility of application of the adaptive models. Nonetheless, this new climate scenario will imply a greater possibility of application of the adaptive models.

44

3 Application of Adaptive Thermal Comfort Models …

Fig. 3.6 Percentage of the days of the year when the adaptive model can be applied (A2 scenario in 2050)

Fig. 3.7 Comparative heat map between the current and A2 scenarios of the percentage of the days of the year when the adaptive model could be applied

The possibility of using natural ventilation has the same relationship according to latitude-altitude (Fig. 3.8). Therefore, the percentage of hours when natural ventilation could be used in regions close to the equator is between 50 and 90% of the hours of the year. In the remaining regions, the use of natural ventilation is limited to spring, summer, and autumn. In zones such as the Mediterranean, high results could be achieved by applying natural ventilation in these months. The coastal zones could obtain percentages of application of 100% of the hours of the summer months. In the interior zones with greater severity, this percentage could be reduced by 35%. In 2050, the percentages of hours when it will be possible to ventilate buildings naturally are expected to change (Fig. 3.9), following the same tendency as that in the percentage of days to apply the adaptive models. Thus, the Earth’s surface where natural ventilation could be used in a percentage of hours greater than 50% will be

3.4 Barriers and Opportunities

45

Fig. 3.8 Percentage of the hours of the year when natural ventilation could be used (current scenario)

Fig. 3.9 Percentage of the hours of the year when natural ventilation could be used (A2 scenario in 2050)

reduced, and in the coldest zones the percentage of hours will be increased. In the Mediterranean region, the use of natural ventilation would be reduced in summer, although the percentage of hours would be increased in spring and autumn. Regarding the effectiveness of adaptive heating setpoint temperatures (Fig. 3.10), maximum savings between 20,800 and 38,000 °C were achieved. These values

46

3 Application of Adaptive Thermal Comfort Models …

Fig. 3.10 Box-plots with the energy saving obtained in heating by using adaptive setpoint temperatures

mainly corresponded to the regions with great severity in winter. In countries located in the Mediterranean region, reductions greater than 5,000 °C were achieved in most of its surface in the assumption of the lowest energy saving (i.e., comparing it with a setpoint temperature of 20 °C). This behavior was also presented in the A2 scenario where the same distributions were almost obtained in the saving values of heating degrees. Regarding the saving in cooling degrees, there were certain differences in comparison with the heating saving (Fig. 3.11). Firstly, cooling savings were greater than heating savings. In this regard, savings oscillated between 24,000 and 38,400 °C, and there were more zones with high saving values in degrees than in the case of heating degrees. Secondly, there was a significant impact of climate change in 2050. Moreover, the use of the adaptive setpoints increased the saving up to 7,000 °C. Thus, the possibility of applying adaptive energy-saving strategies at a global level is high in both the current and future scenarios. The greatest potential of the application of these adaptive models is mainly detected in the cooling energy saving and in the natural ventilation in the warm months. Nonetheless, there are limitations to applying the models throughout the year in the cold regions. For this reason, the effectiveness of the application of the adaptive models has a huge bioclimatic component that should be considered by architects and engineers to establish it as an energy-saving measure. Another limitation is related to the implementation of

3.4 Barriers and Opportunities

47

Fig. 3.11 Box-plots with the energy saving obtained in cooling by using adaptive setpoint temperatures

systems to control the evolution of the adaptive limits. A recent study by BienvenidoHuertas et al. [5] stressed the potential of using weather stations of official agencies to determine the values of the adaptive model in any region. For this purpose, weather stations are not required to be close as they could be accurately estimated by a regression model. This regression model could be easily included in the regulation and control system of the HVAC systems.

3.5 Conclusions The use of adaptive models for building energy saving could be divided into two types: adaptive natural ventilation and adaptive setpoint temperatures. The results obtained in various research studies have shown the great potential of these measures to achieve energy savings mainly in cooling. The use of these techniques could address the existing barriers in energy improvement projects. However, the bioclimatic component of the adaptive thermal comfort models should be considered. Although the existing conditions on most of the Earth’s surface are appropriate to use adaptive models, there are other zones where the possibility of application is limited. Moreover, the effectiveness of the various measures varies according to the region. For

48

3 Application of Adaptive Thermal Comfort Models …

this reason, architects and engineers should consider the most appropriate adaptive measures in each zone. This aspect should be complemented with the small modifications expected in the application of the adaptive models in view of the modification of climate throughout the twenty-first century.

References 1. American Society of Heating Refrigerating and Air Conditioning Engineers (2017) ASHRAE Standard 55-2017 thermal environmental conditions for human occupancy. Atlanta, GA, United States 2. Baek C, Park S (2012) Policy measures to overcome barriers to energy renovation of existing buildings. Renew Sustain Energy Rev 16:3939–3947. https://doi.org/10.1016/j.rser. 2012.03.046 3. Bienvenido-Huertas D, Rubio-Bellido C, Farinha F, et al (2020a) Evaluating the potential of adaptive comfort approach using historic data to reduce energy consumption in buildings in southern Spain. Build Environ 185. https://doi.org/10.1016/j.buildenv.2020.107313 4. Bienvenido-Huertas D, Rubio-Bellido C, Pérez-Fargallo A, Pulido-Arcas JA (2020b) Energy saving potential in current and future world built environments based on the adaptive comfort approach. J Clean Prod 249. https://doi.org/10.1016/j.jclepro.2019.119306 5. Bienvenido-Huertas D, Rubio-Bellido C, Pérez-Ordóñez J, Martínez-Abella F (2019) Estimating Adaptive Setpoint Temperatures Using Weather Stations. Energies 12:1197. https:// doi.org/10.3390/en12071197 6. Bienvenido-Huertas D, Sánchez-García D, Pérez-Fargallo A, Rubio-Bellido C (2020c) Optimization of energy saving with adaptive setpoint temperatures by calculating the prevailing mean outdoor air temperature. Build Environ 170. https://doi.org/10.1016/j.buildenv.2019. 106612 7. Bienvenido-Huertas D, Sánchez-García D, Rubio-Bellido C (2020d) Analysing natural ventilation to reduce the cooling energy consumption and the fuel poverty of social dwellings in coastal zones. Appl Energy 279. https://doi.org/10.1016/j.apenergy.2020.115845 8. Bienvenido-Huertas D, Sánchez-García D, Rubio-Bellido C (2020e) Comparison of energy conservation measures considering adaptive thermal comfort and climate change in existing Mediterranean dwellings. Energy 190. https://doi.org/10.1016/j.energy.2019.116448 9. Bienvenido-Huertas D, Sánchez-García D, Rubio-Bellido C, Oliveira MJ (2020) Influence of adaptive energy saving techniques on office buildings located in cities of the Iberian Peninsula. Sustain Cities Soc 53:101944. https://doi.org/10.1016/j.scs.2019.101944 10. Bodach S, Hamhaber J (2010) Energy efficiency in social housing: opportunities and barriers from a case study in Brazil. Energy Policy 38:7898–7910. https://doi.org/10.1016/j.enpol.2010. 09.009 11. Chai KH, Yeo C (2012) Overcoming energy efficiency barriers through systems approachA conceptual framework. Energy Policy 46:460–472. https://doi.org/10.1016/j.enpol.2012. 04.012 12. Chen X, Zhang Q, Zhai ZJ, Ma X (2019) Potential of ventilation systems with thermal energy storage using PCMs applied to air conditioned buildings. Renew Energy 138:39–53. https:// doi.org/10.1016/j.renene.2019.01.026 13. Chiesa G, Grosso M (2015) Geo-climatic applicability of natural ventilative cooling in the Mediterranean area. Energy Build 107:376–391. https://doi.org/10.1016/j.enbuild.2015.08.043 14. Clark JD, Less BD, Dutton SM et al (2019) Efficacy of occupancy-based smart ventilation control strategies in energy-efficient homes in the United States. Build Environ 156:253–267. https://doi.org/10.1016/j.buildenv.2019.03.002

References

49

15. Dowson M, Poole A, Harrison D, Susman G (2012) Domestic UK retrofit challenge: barriers, incentives and current performance leading into the Green Deal. Energy Policy 50:294–305. https://doi.org/10.1016/j.enpol.2012.07.019 16. European Committee for Standardization (2007) EN 15251:2007 Indoor environmental input parameters for design and assessment of energy performance of buildings addressing indoor quality, thermal environment, lighting and acoustics. European Committee for Standardization, Brussels 17. Faggianelli GA, Brun A, Wurtz E, Muselli M (2014) Natural cross ventilation in buildings on Mediterranean coastal zones. Energy Build 77:206–218. https://doi.org/10.1016/j.enbuild. 2014.03.042 18. Fleiter T, Worrell E, Eichhammer W (2011) Barriers to energy efficiency in industrial bottomup energy demand models—a review. Renew Sustain Energy Rev 15:3099–3111. https://doi. org/10.1016/j.rser.2011.03.025 19. Gil-Baez M, Barrios-Padura Á, Molina-Huelva M, Chacartegui R (2017) Natural ventilation systems in 21st-century for near zero energy school buildings. Energy 137:1186–1200. https:// doi.org/10.1016/j.energy.2017.05.188 20. Heracleous C, Michael A (2018) Assessment of overheating risk and the impact of natural ventilation in educational buildings of Southern Europe under current and future climatic conditions. Energy 165:1228–1239. https://doi.org/10.1016/j.energy.2018.10.051 21. Hiyama K, Glicksman L (2015) Preliminary design method for naturally ventilated buildings using target air change rate and natural ventilation potential maps in the United States. Energy 89:655–666. https://doi.org/10.1016/j.energy.2015.06.026 22. Jeong B, Jeong JW, Park JS (2016) Occupant behavior regarding the manual control of windows in residential buildings. Energy Build 127:206–216. https://doi.org/10.1016/j.enbuild.2016. 05.097 23. Kurekci NA (2016) Determination of optimum insulation thickness for building walls by using heating and cooling degree-day values of all Turkey’s provincial centers. Energy Build 118:197–213. https://doi.org/10.1016/j.enbuild.2016.03.004 24. Nilsson A, Bergstad CJ, Thuvander L et al (2014) Effects of continuous feedback on households’ electricity consumption: potentials and barriers. Appl Energy 122:17–23. https://doi. org/10.1016/j.apenergy.2014.01.060 25. Omrani S, Garcia-Hansen V, Capra BR, Drogemuller R (2017) On the effect of provision of balconies on natural ventilation and thermal comfort in high-rise residential buildings. Build Environ 123:504–516. https://doi.org/10.1016/j.buildenv.2017.07.016 26. Parry ML, Canziani OF, Palutikof JP et al (2007) Contribution of working group II to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge 27. Pisello AL, Castaldo VL, Taylor JE, Cotana F (2016) The impact of natural ventilation on building energy requirement at inter-building scale. Energy Build 127:870–883. https://doi. org/10.1016/j.enbuild.2016.06.023 28. Poputoaia D, Bouzarovski S (2010) Regulating district heating in Romania: legislative challenges and energy efficiency barriers. Energy Policy 38:3820–3829. https://doi.org/10.1016/j. enpol.2010.03.002 29. Ren Z, Chen D (2018) Modelling study of the impact of thermal comfort criteria on housing energy use in Australia. Appl Energy 210:152–166. https://doi.org/10.1016/j.apenergy.2017. 10.110 30. Sánchez-García D, Bienvenido-Huertas D, Tristancho-Carvajal M, Rubio-Bellido C (2019) Adaptive Comfort Control Implemented Model (ACCIM) for Energy Consumption Predictions in Dwellings under Current and Future Climate Conditions: A Case Study Located in Spain. Energies 12:1498. https://doi.org/10.3390/en12081498 31. Sánchez-García D, Rubio-Bellido C, del Río JJM, Pérez-Fargallo A (2019) Towards the quantification of energy demand and consumption through the adaptive comfort approach in mixed mode office buildings considering climate change. Energy Build 187:173–185. https://doi.org/ 10.1016/j.enbuild.2019.02.002

50

3 Application of Adaptive Thermal Comfort Models …

32. Sánchez-García D, Rubio-Bellido C, Marrero Meléndez M et al (2017) El control adaptativo en instalaciones existentes y su potencial en el contexto del cambio climático. Hábitat Sustentable 7:06–17. https://doi.org/10.22320/07190700.2017.07.02.01 33. Sánchez-Guevara Sánchez C, Mavrogianni A, Neila González FJ (2017) On the minimal thermal habitability conditions in low income dwellings in Spain for a new definition of fuel poverty. Build Environ 114:344–356. https://doi.org/10.1016/j.buildenv.2016.12.029 34. Santamouris M, Sfakianaki A, Pavlou K (2010) On the efficiency of night ventilation techniques applied to residential buildings. Energy Build 42:1309–1313. https://doi.org/10.1016/j.enbuild. 2010.02.024 35. Santos HRR, Leal VMS (2012) Energy vs. ventilation rate in buildings: a comprehensive scenario-based assessment in the European context. Energy Build 54:111–121. https://doi.org/ 10.1016/j.enbuild.2012.07.040 36. Schleich J (2009) Barriers to energy efficiency: a comparison across the German commercial and services sector. Ecol Econ 68:2150–2159. https://doi.org/10.1016/j.ecolecon.2009.02.008 37. Schleich J, Gruber E (2008) Beyond case studies: Barriers to energy efficiency in commerce and the services sector. Energy Econ 30:449–464. https://doi.org/10.1016/j.eneco.2006.08.004 38. Schulze T, Eicker U (2013) Controlled natural ventilation for energy efficient buildings. Energy Build 56:221–232. https://doi.org/10.1016/j.enbuild.2012.07.044 39. Sovacool BK (2009) The cultural barriers to renewable energy and energy efficiency in the United States. Technol Soc 31:365–373. https://doi.org/10.1016/j.techsoc.2009.10.009 40. Spindler HC, Norford LK (2009) Naturally ventilated and mixed-mode buildings-Part I: thermal modeling. Build Environ 44:736–749. https://doi.org/10.1016/j.buildenv.2008.05.019 41. Spyropoulos GN, Balaras CA (2011) Energy consumption and the potential of energy savings in Hellenic office buildings used as bank branches—a case study. Energy Build 43:770–778. https://doi.org/10.1016/j.enbuild.2010.12.015 42. The Government of Spain (2006) Royal Decree 314/2006. Approving the Spanish Technical Building Code, Madrid, Spain 43. The Government of Spain (2014) Royal Decree 216/2014, of 28 March, sets out the methodology for the calculation of the Voluntary Price for the Small Consumer 44. Tong Z, Chen Y, Malkawi A (2017) Estimating natural ventilation potential for high-rise buildings considering boundary layer meteorology. Appl Energy 193:276–286. https://doi.org/10. 1016/j.apenergy.2017.02.041 45. Tong Z, Chen Y, Malkawi A et al (2016) Energy saving potential of natural ventilation in China: The impact of ambient air pollution. Appl Energy 179:660–668. https://doi.org/10.1016/j.ape nergy.2016.07.019 46. Tuominen P, Klobut K, Tolman A et al (2012) Energy savings potential in buildings and overcoming market barriers in member states of the European Union. Energy Build 51:48–55. https://doi.org/10.1016/j.enbuild.2012.04.015 47. Tushar W, Wang T, Lan L et al (2017) Policy design for controlling set-point temperature of ACs in shared spaces of buildings. Energy Build 134:105–114. https://doi.org/10.1016/j.enb uild.2016.10.027 48. Vine EL, Kazakevicius E (1999) Residential energy use in Lithuania: The prospects for energy efficiency. Energy 24:591–603. https://doi.org/10.1016/S0360-5442(99)00013-4 49. Wan KKW, Li DHW, Lam JC (2011) Assessment of climate change impact on building energy use and mitigation measures in subtropical climates. Energy 36:1404–1414. https://doi.org/10. 1016/j.energy.2011.01.033 50. Wang T, Li X, Liao PC, Fang D (2016) Building energy efficiency for public hospitals and healthcare facilities in China: barriers and drivers. Energy 103:588–597. https://doi.org/10. 1016/j.energy.2016.03.039 51. Yun GY, Lee JH, Steemers K (2016) Extending the applicability of the adaptive comfort model to the control of air-conditioning systems. Build Environ 105:13–23. https://doi.org/10.1016/ j.buildenv.2016.05.027 52. Zhang Y, Wang Y (2013) Barriers’ and policies’ analysis of China’s building energy efficiency. Energy Policy 62:768–773. https://doi.org/10.1016/j.enpol.2013.06.128

Chapter 4

Energy Savings Obtained with an Adaptive Approach with Respect to Building Envelope Improvement

Abstract Strategies based on adaptive thermal comfort models have great potential for application in most parts of the world. This means that these strategies are appropriate bioclimatic measures for buildings and constitute a tool for architects and engineers. However, it is necessary to know quantitatively the energy savings expected with this type of strategy. For this reason, this chapter analyzes the energy savings obtained in two buildings. The analysis was carried out in different climatic zones in Spain and using an adaptive strategy based on the three categories of EN 16798-1:2019. The results show the total energy savings obtained with the adaptive strategies: Category I ranged from 6.8 to 30.4%, Category II ranged from 23 to 56.3%, and Category III from 35.8 to 74.6%. Furthermore, the use of these strategies is adequate to reduce the payback periods of other energy conservation measures (e.g., the increase in thermal resistance of the facade), with reductions of up to 42 years in Category I, 64 years in Category II, and 73 years in Category III. Keywords Thermal comfort · Adaptive comfort · Energy saving · Payback periods · Energy conservation measures · Bioclimatic design · Energy efficiency

4.1 Introduction Adaptive thermal comfort models are an opportunity to achieve building energy savings without making high economic investments, so they are an appropriate measure to intervene in the short/medium term in the family units in an Energy poverty (EP) situation. However, other studies have shown that the combination of adaptive measures with other Energy conservation measures (ECMs) (e.g., façade improvement) could also be an opportunity to achieve the low-carbon goals for the building stock [1]. This chapter aims to discuss more deeply the potential of the adaptive thermal comfort strategies by both analyzing the energy saving in case studies and assessing the effectiveness of their use combined with other ECMs. For this purpose, two case studies located in Andalusia (in the south of Spain) were analyzed. As mentioned in Chap. 3, the percentages of applying the adaptive models in this region are greater © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 D. Bienvenido-Huertas and C. Rubio-Bellido, Adaptive Thermal Comfort of Indoor Environment for Residential Buildings, SpringerBriefs in Architectural Design and Technology, https://doi.org/10.1007/978-981-16-0906-0_4

51

52

4 Energy Savings Obtained with an Adaptive Approach …

Fig. 4.1 Building census in Andalusia

than 50% of the days of the year. In addition, there are three other interesting aspects to consider this region for the goals proposed in this chapter: (i) the building stock in this region was built before the development of the first energy efficiency standard in the country, i.e., before 1980 (Fig. 4.1). This means that envelope designs in most buildings are not appropriate, thus implying great heat transfer. (ii) The greatest unemployment and monetary poverty data belongs to families living in this region [3]. (iii) Most of the microclimates existing in Spain are found in this region, so the results can be extrapolated to the whole country.

4.2 Case Study To analyze the effectiveness of the energy improvement achieved in buildings by applying the adaptive setpoint temperatures, the energy improvement obtained in two recent case studies designed according to the technical specifications established by the Spanish Building Technical Code (CTE) was assessed. Figure 4.2 represents a sketch of the two case studies. These two typologies of case studies correspond to the most usual structure of residential buildings in Spain, particularly in Andalusia. Case Study 1 corresponds to a rectangular floor building of three floors, each with six dwelling units (the surface area of the typical floor is 437.6 m2 ), and Case Study 2 corresponds to a rectangular floor building of eight floors, each with two dwelling units (the surface area of the typical floor is 158 m2 ). The case studies were analyzed according to the energy analysis criterion established by the CTE both

4.2 Case Study

53

Fig. 4.2 Case studies analyzed in the research

for the verifications of the fulfillment of the building energy consumption and for the energy certification. Both case studies were therefore analyzed by using the profile of operational conditions for residential buildings defined by the CTE (Fig. 4.3). The occupancy during working days varied throughout the day, achieving 100% from

Fig. 4.3 Percentage distribution of the hourly load established by the operational profile of the CTE for residential buildings

54

4 Energy Savings Obtained with an Adaptive Approach …

00:00 to 7:00. As for the weekend, there was a total and continuing occupancy. The use of equipment and lighting had the same operational profile, achieving 100% between 20:00 and 23:00. The maximum loads established (i.e., when the percentage was 100%) were 2.15 W/m2 for the sensible occupancy, 1.36 W/m2 for the latent occupancy, 4.40 W/m2 for lighting, and 4.4 W/m2 for equipment. Regarding the performance of HVAC systems, as the analysis was based on the hypothesis to improve the performance of new buildings, it was proposed that buildings had a heat pump with an EER of 3.67 and a COP of 4.55. Regarding the operation of these systems, the values analyzed for the setpoint temperatures varied according to the approach used (the approach of static setpoint temperatures established by the CTE or the approach of adaptive setpoint temperatures). Table 4.1 includes the hourly values assigned to heating and cooling setpoint temperatures. Regarding the static approach, the setpoint temperatures established by the CTE were used: 25 or 27 °C for cooling depending on the hour of the day, and 17 or 20 °C for heating depending on the hour of the day, as well. Regarding the adaptive setpoint temperatures, three different approaches were analyzed, each corresponding to each category from EN 16798-1:2019. The effect of progressively increasing users’ thermal expectations was therefore verified. It is worth stressing that both static and adaptive setpoints were adjusted to the use schedule of the HVAC systems established by the CTE, which is characterized by considering that HVAC systems are not used from 07:00 to 15:00 in the summer. These two buildings were analyzed in all climate zones of the CTE. The climate classification established by the CTE is based on the combination of two indicators: Winter Climate Severity (WCS) and Summer Climate Severity (SCS). These indicators are determined by using correlations with the degree days in winter and summer (using a base temperature of 20 °C), and depending on the value obtained, a number or letter is allocated (see Table 4.2). The combination of WCS and SCS generates a total of 15 different combinations as not all combinations of WCS and SCS take place in the climate zones of the country (e.g., Zone E4 does not exist). Based on the analysis of the zones obtained in Andalusia, it is verified that there is a combination of 10 different zones of the CTE, including all typologies of SCS (1, 2, 3, and 4) and WCS (A, B, C, D, and E) (Fig. 4.4), except the zone α is used for some municipalities of the Canary island territories. The use of energy-saving strategies with adaptive setpoint temperatures is therefore very likely in the Spanish territory. To guarantee an adjustment of the building design to the CTE’s specifications, the limit values of the thermal properties of the envelope were adjusted to those established by the CTE for each climate zone. These limit values were determined according to the label of WCS of the building (Table 4.3). By using this criterion and given that all winter zones take place in Andalusia except zone α, a total of five different models were designed for each case study. These configurations of operational conditions and thermophysical properties of envelopes were used to simulate buildings with EnergyPlus. The analysis was performed by locating the building in a representative city of the 10 existing climate zones in Andalusia. Moreover, Meteonorm software was used to obtain the EnergyPlus weather files.

C-II

Heating

Cooling

Heating

15:00–22:59

(4) 24.7

10 ≤ Tr m < 30

Tr m > 30

–

Tr m > 30 18.1

–

10 ≤ Tr m < 30

Tr m < 10

–

25.7

Tr m > 30

Tr m < 10

(2)

10 ≤ Tr m < 30

–

Tr m > 30 19.1

–

10 ≤ Tr m < 30

Tr m < 10

–

17

24.7

(4)

18.1

–

–

–

25.7

(2)

19.1

–

–

–

20

24.7

(4)

18.1

–

–

–

25.7

(2)

19.1

–

–

–

20

–

–

–

–

31.7

(3)

25.1

–

–

–

30.7

(1)

24.1

–

27

23:00–06:59

–

07:00–14:59

23:00–06:59 –

June–September

January–May October–December

Setpoint temperature [°C]

Tr m < 10

All

Cooling

Heating

C-I

All

Cooling

Sta.

Range [°C]

System

Approach

Table 4.1 Approaches of setpoint temperatures analyzed in the research

–

–

–

–

–

–

–

–

–

–

–

–

–

–

07:00–14:59

–

–

–

31.7

(3)

25.1

–

–

–

30.7

(1)

24.1

–

25

(continued)

15:00–22:59

4.2 Case Study 55

Cooling

C-III

15:00–22:59

17.1 (6) 23.7

10 ≤ Tr m < 30

Tr m > 30

–

Tr m > 30

Tr m < 10

–

10 ≤ Tr m < 30

23.7

(6)

17.1

–

–

23.7

(6)

17.1

–

–

–

–

32.7

(5)

26.1

23:00–06:59

–

07:00–14:59

23:00–06:59 –

June–September

January–May October–December

Setpoint temperature [°C]

Tr m < 10

Range [°C]

–

–

–

–

07:00–14:59

(1) 0.33 · Tr m + 20.8; (2) 0.33 · Tr m + 15.8; (3) 0.33 · Tr m + 21.8; (4) 0.33 · Tr m + 14.8; (5) 0.33 · Tr m + 22.8; (6) 0.33 · Tr m + 13.8

Heating

System

Approach

Table 4.1 (continued)

–

–

–

32.7

(5)

26.1

15:00–22:59

56 4 Energy Savings Obtained with an Adaptive Approach …

4.2 Case Study

57

Table 4.2 Classification intervals for WCS and SCS Intervals for WCS

Intervals for SCS

Class

Class

Value

Value

α

WCS ≤ 0

1

SCS ≤ 0.50

A

0 < W C S ≤ 0.23

2

0.50 < SC S ≤ 0.83

B

0.23 < W C S ≤ 0.50

3

0.83 < SC S ≤ 1.38

C

0.50 < W C S ≤ 0.93

4

SCS > 1.38

D

0.93 < W C S ≤ 1.51

E

W C S > 1.51

Fig. 4.4 Climate zones in the south of Spain from the CTE Table 4.3 Maximum thermal transmittance values established by the CTE for the opaque and glazed elements of building envelopes Element

Maximum thermal transmittance [W/(m2 K)] Winter climate zone α

A

B

C

D

E

Wall

0.80

0.70

0.56

0.49

0.41

0.37

Elements in contact with the ground

0.90

0.80

0.75

0.70

0.65

0.59

Party wall

0.90

0.80

0.75

0.70

0.65

0.59

Roof

0.55

0.50

0.44

0.40

0.35

0.33

Floor in contact with air

0.80

0.70

0.56

0.49

0.41

0.37

Window

3.2

2.7

2.3

2.1

1.8

1.8

58

4 Energy Savings Obtained with an Adaptive Approach …

4.3 Energy Saving with Adaptive Measures The results showed that there was a tendency in the reduction of the schedule energy consumption in both case studies. To visualize this aspect, Figs. 4.5 and 4.6 represent the dispersion diagrams of the schedule energy consumption obtained by each approach. There were two different behaviors according to the type of energy consumption. • As for heating energy consumption, there were two tendencies: on the one hand, a greater energy consumption obtained by the adaptive strategies, and on the other hand, a lower energy consumption obtained by the adaptive setpoint temperatures. This aspect was due to the values of the static setpoint temperatures with which they were compared. As mentioned above, the CTE considers two static setpoint temperatures: 17 and 20 °C. The temperature of 17 °C for the night period was an effective static setpoint temperature that implied that the adaptive setpoint temperatures were always high (the lowest value achieved by the adaptive thermal comfort model was 17.1 °C). Under this circumstance, the consumption with this static setpoint temperature was always lower than that with the adaptive setpoints, thus explaining the tendency of greater energy consumption. The lowest energy consumption obtained with adaptive setpoints took place in the hourly range when a static setpoint temperature of 20 °C was used. This aspect therefore shows the possible influence of the residents’ usual use of heating systems on the effectiveness of adaptive strategies. Generally, people are quite aware of the need for using low setpoint temperatures for heating systems, although adopting adaptive strategies would guarantee better use of heating systems. • The greatest potential of using adaptive setpoint temperatures was based on the use of cooling systems. In this regard, adaptive setpoint temperatures significantly reduced the schedule energy consumption of the case studies in all categories. Although Category I (i.e., the category with the lowest thermal adaptation) achieved a lower energy saving than Category III, the results obtained with all categories were hourly savings of up to 5 kWh in Category I and 6 kWh in Category III. This schedule energy consumption was therefore reduced with the adaptive setpoint temperatures, although for heating, the use of a static setpoint value of 17 °C could generate greater energy consumption with an adaptive approach. Nonetheless, the effect annually generated using adaptive setpoints allows significant savings to be obtained in both types of consumption in comparison with a static operational approach. To analyze this aspect, Figs. 4.7 and 4.8 show the annual energy consumption obtained in both case studies. The annual cooling energy saving was quite significant, with percentage deviations in comparison with the static model between 53 and 86% in Category I, between 67.7 and 95.9% in Category II, and between 79.1 and 99.3% in Category III. In most climate zones, Category III could almost eliminate cooling energy consumption. Regarding heating energy consumption, when Category I increased energy consumption because of the difference existing between

4.3 Energy Saving with Adaptive Measures

59

Fig. 4.5 Dispersion diagrams with the cooling (blue) and the heating (red) energy consumption obtained by both the static approach and the adaptive approach in Case 1

60

4 Energy Savings Obtained with an Adaptive Approach …

Fig. 4.6 Dispersion diagrams with the cooling (blue) and the heating (red) energy consumption obtained by both the static approach and the adaptive approach in Case 2

4.3 Energy Saving with Adaptive Measures

61

Fig. 4.7 Annual energy consumption (cooling, heating, and total) obtained in Case 1 by the various operational approaches of HVAC systems

Fig. 4.8 Annual energy consumption (cooling, heating, and total) obtained in Case 2 by the various operational approaches of HVAC systems

62

4 Energy Savings Obtained with an Adaptive Approach …

adaptive setpoints and the static setpoint of 17 °C, the remaining categories obtained savings in comparison with the energy consumption of static setpoints: Category II achieved a saving between 5.4 and 12%, and Category III between 22.8 and 42%. The use of the categories with greater thermal adaptation in cold seasons would therefore achieve a significant energy saving. These energy savings greatly affected the total energy consumption. Although Category I did not reduce the energy consumption with heating systems, savings between 6.8 and 30.4% were obtained at a total level due to the saving obtained with cooling systems. The total saving was greater with the other two categories: Category II ranged from 23 to 56.3% and Category III from 35.8 to 74.6%. It is also worth stressing the relationship between the climate zone of the building and the energy efficiency achieved by the adaptive strategies. The warmest climate zones obtained a greater energy saving with the adaptive setpoint temperatures, and zone E1 obtained the lowest saving. Although this aspect could mean a use limitation of the adaptive setpoint temperatures, it could be very important in view of the expectations of climate variation throughout the twenty-first century. In this regard, the Special Report on Emissions Scenarios (SRES), included in the Fourth Assessment Report (AR4) [2] of the Intergovernmental Panel on Climate Change (IPCC) in 2007, consider that the annual average temperature could be increased between 1.1 and 6.4 °C. Based on these predictions, the adoption of adaptive setpoint temperatures is likely to present a greater potential of use in the coldest climate zones due to the progressive increase of the external temperature, whereas the effectiveness in the warmest zones will be greater, thus also ensuring a resilient energy-saving strategy of buildings according to the climate evolution expected in the following years.

4.4 Combination of Adaptive Measures with Other Energy Conservation Measures: Payback Periods Adaptive setpoints are therefore an effective strategy to reduce the energy consumption of buildings having the envelope and the HVAC systems designed according to the criteria established by the state regulation. However, most energy efficiency projects are focused on the energy improvement of the existing building stock as these buildings could have greater energy consumption and in turn greater greenhouse gas emissions. Thus, the effect of considering adaptive setpoint temperatures in energy rehabilitation projects was analyzed. For this purpose, the geometries of the two case studies considered in the previous section were analyzed, considering that these buildings were designed before the NBE-CT-79 (i.e., before the development of the standards on building energy efficiency). The building has a wall of double-leaf brick without isolation (whose thermal transmittance is 1.61 W/(m2 K)). In addition, the windows are monolithically glazed with a thickness of 4 mm and with a framework without thermal bridge break.

4.4 Combination of Adaptive Measures with Other Energy Conservation Measures …

63

A total of 22 ECMs related to the decrease of the thermal transmittance of walls was considered (Fig. 4.9). These ECMs consider various possibilities to improve walls, such as External Thermal Insulation Composite Systems (ETICS), insufflate or

Fig. 4.9 Compilation of the ECMs analyzed in the research

64

4 Energy Savings Obtained with an Adaptive Approach …

plasterboard. In view of these ECMs, the quickest way to determine the profitability of the economic investment is by analyzing the payback period. This analysis is based on the time required for the return of energy rehabilitation costs (i.e., the investment flow) with the saving from the energy bill (i.e., the return cash flow). The payback period was therefore obtained by amortizing the investment flow through annual return cash flows (Eq. (4.1)), thus determining the number of years required to recover the economic investment. Payback period = N j−1 +

i 0 − R j−1 rj

(4.1)

where N j−1 [years] is the number of years before the year of amortization j, i 0 [e] is the investment cost of the ECM, R j−1 [e] is the return cash flow accumulated before the year j, and r j [e] is the return cash flow in the year j. A procedure to determine the amount of the electricity bill is required for this analysis. In Spain, there are four main factors in the electricity bill that influence the value paid by a family unit: the energy term, the power term, the electricity tax, and the value-added tax. For this research, the prices established in a tariff without hourly discrimination from an electricity company in which the price of the kWh is always the same were used: a value of 0.1349 e/kWh was considered for the energy term and a price of 0.1233 e/kWday for the power term. The electricity tax was considered with a value of 5.113% of the price value of the energy and power term, whereas the value-added tax was 21% of the remaining concepts included in the electricity bill (i.e., the sum of the price of the energy term, the power term, the value-added tax, and little concepts, such as the rent of meters). By analyzing the results of the payback periods (Figs. 4.10 and 4.11), the combination of the ECM and the use of adaptive setpoint temperatures significantly reduced them. In some cases, the payback periods could be long over time (e.g., the ECM from ETICS in zones A3, A4, B3, or B4); however, the combination of these ECMs with adaptive setpoint temperatures significantly reduced the number of years required for recovering the economic investment, with reductions of up to 42 years in Category I, 64 years in Category II, and 73 years in Category III. Nevertheless, these high savings in the payback periods were related to the high investment required to apply ETICS. Although this type of ECM has advantages such as the reduction of the linear thermal transmittance, the high economic cost could limit its implementation. However, with an operational approach based on adaptive thermal comfort models, the energy saving achieved significantly reduced the number of years required for recovering the economic investment. As for the ECMs with a lower cost (ECMs 18, 19, 20, and 21), although the payback periods were lower than 25 years, the use of approaches of adaptive setpoints also reduced the number of years of the payback period, even recovering in some cases the economic investment in the same year. So, the use of adaptive setpoint temperatures, together with usual ECMs in energy rehabilitations, not just would allow a lower building energy consumption than that obtained by simply applying the ECM, but also constitutes a strategy to obtain a shorter payback period.

4.5 Conclusions

65

Fig. 4.10 Years required for recovering the economic investment in the improvement of Case 1 (climate zones A3, A4, B3, and B4)

4.5 Conclusions Building energy improvement is crucial to both reduce greenhouse gases emitted into the atmosphere and mitigate the impact of climate change. Likewise, the improvement of the building energy efficiency would guarantee better habitability conditions for users, increase the economic assessment of dwellings, and reduce the EP of family units. However, the adoption of energy improvement measures could be economically, technically, and institutionally limited, thus making the rehabilitation of the building stock something of a challenge. For this reason, the adoption of energysaving strategies based on adaptive thermal comfort models could be an opportunity to achieve important energy savings with a low economic cost. The use of these setpoint temperatures reduces energy consumption by modifying the operational patterns of the HVAC systems without making high economic investments but guaranteeing always the users’ thermal comfort. Setpoint temperatures could therefore be an appropriate measure in view of both the lack of families’ economic resources

66

4 Energy Savings Obtained with an Adaptive Approach …

Fig. 4.11 Years required for recovering the economic investment in the improvement of Case 2 (climate zones A3, A4, B3, and B4)

to carry out improvement performances and institutional support limitations. If an energy conservation measure is implemented, such as the façade improvement, the combination of this improvement with the use of adaptive setpoints significantly reduces payback periods, thus guaranteeing a greater economic profitability of the improvement carried out in the building. The results of this chapter are of importance to ensure a greater rehabilitation rate of the existing building stock and to improve the thermal performance of the buildings designed according to the current standards on building energy efficiency. Moreover, the use of these strategies guarantees a greater resilience of the building stock in future climate change scenarios.

References

67

References 1. Bienvenido-Huertas D, Sánchez-García D, Rubio-Bellido C (2020) Comparison of energy conservation measures considering adaptive thermal comfort and climate change in existing Mediterranean dwellings. Energy 190. https://doi.org/10.1016/j.energy.2019.116448 2. Randall DA, Wood RA, Bony S, Colman R, Fichefet T, Fyfe J, Kattsov V, Pitman A, Shukla J, Srinivasan J, Stouffer RJ, AS, Taylor and KE (2007) Climate Models and Their Evaluation 3. Spanish Institute of Statistics (2019) Atlas of distribution of household income. Retrieved from: https://www.ine.es/experimental/experimental.htm

Chapter 5

Decision-Making in Applying Adaptive Approaches in Indoor Spaces

Abstract Adaptive thermal comfort strategies allow to achieve significant savings in the energy consumption of a building. This represents great potential for buildings since it allows to guarantee thermal comfort and reduce energy consumption without the need for economic investments. However, decision-making can make it difficult to implement the most appropriate strategy. For this reason, this chapter analyzes the single criteria and multi-criteria process to determine the most appropriate strategy. For this, fuzzy logic is used. With the fuzzy logic, two expert systems were designed: one for rehabilitation works (affected by the improvement achieved and the investment price) and another for new buildings. These models analyzed four case studies. The results obtained have shown that the systems designed with fuzzy logic have an adequate success rate with respect to the expected decision. Therefore, they constitute an adequate methodology for decision-making with respect to the thermal comfort model. In addition, architects and engineers can make modifications to the structure of the systems to adapt them to different regions. Keywords Criteria selection · Thermal comfort · Adaptive comfort · Fuzzy logic · Restoration works · New buildings · Energy efficiency

5.1 Introduction Adaptive setpoint temperatures could therefore be referenced as a measure both for the energy saving in the buildings designed according to the current standards on energy efficiency and for the reduction of the payback periods of other ECMs that could be implemented in existing buildings. However, the category from EN 16798-1:2019 used to determine adaptive setpoint temperatures influences the energy savings obtained. Although Category III obtained the greatest energy savings, it did not present a high percentage of acceptability on the part of users. This category could therefore not be applied in all cases as it could affect the users’ thermal comfort. To guarantee an optimal implementation, the category of adaptive thermal comfort to be used should be adapted both to the users’ thermal expectations and to the energy and economic demands of the project. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 D. Bienvenido-Huertas and C. Rubio-Bellido, Adaptive Thermal Comfort of Indoor Environment for Residential Buildings, SpringerBriefs in Architectural Design and Technology, https://doi.org/10.1007/978-981-16-0906-0_5

69

70

5 Decision-Making in Applying Adaptive Approaches …

Thus, it is crucial to establish decision-making processes to have a common framework to implement adaptive strategies. In this regard, decision-making processes are used in many scopes of architecture and engineering (e.g., the restoration of historic buildings) to determine the solution that should be implemented. This chapter presents various decision-making approaches for the adaptive thermal comfort model that should be implemented in new or restored building projects.

5.2 Decision-Making Processes The decision-making processes developed here are divided into two types: single and multi-criteria processes.

5.2.1 The Single Criteria Process A simple selection process could be carried out by analyzing certain variables. In this regard, a clear variable that could be used in a single criteria process is the users’ thermal adaptation. If the users of the building have high thermal adaptation, Category III from EN 16798-1:2019 could be used, whereas Category I should be used for users with low thermal adaptation. However, the analysis cannot be performed in many cases based on users’ thermal adaptation, so other variables should be analyzed to establish the most appropriate category to determine the adaptive setpoint temperatures. The variables that could therefore be analyzed in a single criteria process to determine the category of adaptive thermal comfort in new buildings are as follows. • • • •

Generation of electrical energy in the building (e.g., photovoltaic systems). Performance of HVAC systems. Climate zone of the building. Shadows from both the elements of the building and buildings or other urban elements producing shadows and varying the building energy demand. • Estimated incomes of the users living in the building. In restored buildings, the following variables, along with the variables included in the previous list, could be used. • Price of the energy improvement measures. • Thermal performance of the opaque parts of the envelope after an intervention. • Thermal performance of windows after an intervention.

5.2 Decision-Making Processes

71

5.2.2 The Multi-criteria Process In many cases, the individual analysis of the variables of the previous section for a single criteria process does not determine correctly the most appropriate category to be implemented. This is due to the many aspects characterizing a building in which the individual analysis of the different variables could provide various responses about which category should be used. For this reason, a multi-criteria process could be appropriate when a different response is estimated for the variables considered in the previous section. To guarantee a multi-criteria process that includes the changing nature of the input variables, expert systems based on fuzzy logic were designed for decision-making. The theory of fuzzy logic was introduced by González Morcillo 1965 [1]. This theory consists of a typology of multi-valued logic that mathematically represents uncertainty and vagueness, providing formal tools for their treatment; in other words, this theory is a type of logic based on the theory of sets that tries to copy the reasoning method usually used by people in their daily life [3]. According to this theory, the variables in the universe do not have a black and white duality, but there is a wide range of grays responding to the various stages or possibilities. Fuzzy logic therefore allows for imprecise information to be treated in terms of fuzzy sets, such as low or high thermal adaptation. These fuzzy sets are combined with each other by rules to define actions. Thus, the expert systems based on fuzzy logic combine a series of input variables by using terms of fuzzy sets and predicting an output value through a series of rules. It is worth noting that the concept of fuzzy logic is different from the concept of probability, although they are related to each other in a certain way. Probability represents information on the frequency of relative ideas of a well-defined event over the total number of possible events, whereas the fuzzy logic represents the similarities of an event with respect to another event, where the properties of these events are not precisely defined [1]. For a multi-criteria analysis, two expert systems were designed: one for restoration works (affected by the improvement achieved and the investment price) (Fig. 5.1) and another for new buildings (Fig. 5.2). The input variables used in the systems were those presented for a single criteria process, and the only difference presented by the systems for new and restored buildings is that the latter used both the price variables of the economic investment and the thermal performance improvement of the envelope and windows. The fuzzy systems have four stages: fuzzifier, fuzzy rules bases, inference, and defuzzifier. The fuzzifier establishes a relationship between the values of the input variables and the fuzzy sets. In general terms, all input variables presented the same structure of a fuzzy set, with membership functions of type bell (as they allow a tolerance margin around the value taken as characteristic of the linguistic term of

72

5 Decision-Making in Applying Adaptive Approaches …

Fig. 5.1 Scheme of the fuzzy logic model designed to be applied in building restorations

Fig. 5.2 Scheme of the fuzzy logic model designed to be applied in new buildings

the fuzzy set) and three terms in a universe of discourse (U) between 0 and 1. The only exception was the income variable. Due to the possible options, five terms in the universe of discourse were adopted. Figure 5.3 shows the membership functions and the terms for each variable. The value of the output variable used the defuzzifier method per the center of area. In addition, the computational tool of the free software called Xfuzzy 3.5 [2] was used to develop the model (Fig. 5.4). It is worth stressing that this tool is a development environment for inference systems based on fuzzy logic that combines a set of tools that facilitates the various stages of the process, from its initial description to the final implementation, with a flexibility that constitutes one of its main characteristics to develop complex systems, thus defining the functions of the fuzzy set [3].

5.2 Decision-Making Processes

73

Fig. 5.3 Characteristics of the input and output variables used in the fuzzy logic models

The models implemented in Xfuzzy were used to assess the quality of the given response. For this purpose, four case studies were analyzed (Table 5.1). These case studies were social dwellings built in Cadiz and Seville; these social dwellings were recently built or are old buildings. The estimate carried out by the expert models was adjusted to the optimal response determined in the case studies. The fuzzy logic models designed could therefore be used in a multi-criteria analysis to determine the most appropriate operational guidelines of HVAC systems for users of those buildings that require an energy restoration or buildings recently built.

74

5 Decision-Making in Applying Adaptive Approaches …

Fig. 5.4 Implementation of the rehabilitation model in Xfuzzy Table 5.1 Results of the estimates conducted by fuzzy logic systems Case study

Input variables

Expected category

Estimated category

Case 1 (rehabilitation)

Generation = Low Thermal adaptation = Low Climate zone = Warm Income = Low Price = Medium Shadow = Medium Heat transfer envelope = High Heat transfer window = High HVAC performance = High

Category II

Category II

Case 2 (rehabilitation)

Generation = Low Thermal adaptation = Low Climate zone = Warm Income = High Price = Medium Shadow = Medium Heat transfer envelope = High Heat transfer window = High HVAC performance = High

Category I

Category I

(continued)

5.3 Conclusions

75

Table 5.1 (continued) Case study

Input variables

Expected category

Estimated category

Case 3 (new building)

Generation = High Thermal adaptation = Low Climate zone = Warm Income = Low Shadow = Medium HVAC performance = Medium

Category I

Category I

Case 4 (new building)

Generation = Low Thermal adaptation = Medium Climate zone = Medium Income = Medium Shadow = Low HVAC performance = High

Category II

Category II

5.3 Conclusions Determining the most appropriate adaptive approach to be implemented in a building project (new or restored) could be a great challenge. Two decision-making approaches could be considered. A simple-criteria approach could consider some of the main variables related to building energy performance, although it makes the decisionmaking difficult. So, the use of a multi-criteria approach is most appropriate. To ease the decision-making process about the most appropriate thermal comfort category to be implemented, fuzzy logic builds up expert models that include the analysis of different variables related to building and economic aspects. These systems determine the most appropriate category to be implemented based on both economic and technical requirements and users’ thermal expectations. The results have shown that the responses given by the systems designed with fuzzy logic present an appropriate true positive rate in comparison with the response expected. Thus, expert models are a more appropriate methodology for decision-making than the thermal comfort model. In addition, the structure presented by the models designed with fuzzy logic allows increases and modifications to be implemented in the structure proposed by this work. Consequently, architects and engineers could establish the appropriate modifications to adapt the models’ response to the context where models are used.

References 1. González Morcillo C (2011) Fuzzy Logic, a practical introduction. Softcomputing techniques 29 2. Instituto de Microelectrónica de Sevilla (IMSE-CNM) (2003) Fuzzy Logic Design Tools. 54 3. Martín del Río B, Sanz Molina A (2006) Neural networks and fuzzy systems