A Review on Adaptive Thermal Comfort of Office Building for Energy-Saving Building Design

Abstract

The thermal environment quality of office buildings has an important role because thermal comfort is directly related to human productivity. Thermal comfort conditions are influenced by climate, location, and the built environment; hence, comfort standards are required to assist building designers in creating a comfortable indoor environment for building occupants. In this context, the present study analyzes the adaptive thermal comfort studies conducted in office buildings from various countries. A large number of research articles selected from the Scopus database were considered for this study. Based on the analysis, outdoor climatic conditions have a greater influence on indoor thermal conditions in naturally ventilated than in air-conditioned office buildings. The temperature required for comfort is as low as 17.6 °C and as high as 31.2 °C in naturally ventilated buildings. An adaptive comfort equation for naturally ventilated and air-conditioned office buildings has also been proposed to predict the indoor comfort temperature. Various studies show that a substantial amount of energy can be saved by changing the set point and natural ventilation. Furthermore, this study successfully provides hearty evidence that there is a need for climate-specific standards on thermal comfort for energy-efficient design development because existing comfort standards might not be applicable to all climates.

1. Introduction

1.1. Overview

People spend eighty-seven percent of their time in indoor climates [1]. As a result, research on indoor thermal comfort has dramatically increased. Thermal comfort is defined as “a condition of mind that expresses satisfaction with the thermal environment” [2], which indicates that thermal comfort is subjective. The most common method for defining thermal comfort conditions is through subjective evaluation, which is performed by monitoring each subject’s thermal feelings, preferences, and physical and personal comfort factors over a group of subjects in a field or a lab [2]. Thermal comfort studies conducted over the years suggest that the thermal comfort condition is influenced by climate, geographical location, and the built environment [3,4,5,6]. Thermal comfort standards are required to assist building designers in providing a thermally comfortable indoor environment for building occupants [7,8]. Thermal discomfort reduces human productivity [9,10,11], so thermal sensation may play an important role, particularly in office buildings. This has prompted scientists and researchers to conduct thermal comfort studies in office buildings around the world, covering various climates and built environments and proposing an adaptive model [12,13,14,15,16,17,18,19,20,21,22,23,24].

1.2. Adaptive Thermal Comfort

The adaptive approach to thermal comfort considers how individuals interact with their thermal environment [3,6,25]. A model that relates indoor design temperatures or acceptable temperature ranges to outdoor temperature is known as an adaptive model [2]. According to the adaptive method, if a change causes discomfort, people will respond in a way that tends to make them feel more comfortable [3,6,7,26,27,28,29,30]. The adaptive approach to thermal comfort was developed from field studies of people in daily life and emphasizes to the interaction between people and their thermal environment [3,6,25,31]. This approach considers how people interact with their thermal surroundings. The adaptable approach, particularly in free-running buildings, allows building designers to predict the indoor temperature that building occupants are most likely to find comfortable [7]. Office workers have limited adaptation opportunities compared to those in other building types because of the shared use of space and organizational culture [32,33]. As a result, the type of building, in terms of providing adaptive opportunities to users, can have a significant contextual influence on occupants’ thermal experiences. However, it is claimed that the thermal comfort recommendations provided by current international standards, such as ASHRAE Standard 55 [2] and EN 15251 [34], are applicable to a variety of building types. Given that the adaptive comfort recommendations used in the current standards, such as ASHRAE RP-884 [35] and SCATS [27], were created from field data almost acquired from offices. the following three categories of adaptation [5,6,36] determine the flexibility and scope of adaptation.

a)

Behavioral adaptation: Conscious and unconscious actions taken by subjects, which in turn change the body’s mass and heat fluxes. It functions on three levels: personal, technological, and cultural.

b)

Physiological adaptation: Long-term exposure causes changes in occupants, such as the prevailing climate having an impact on the occupants themselves. Again, it comes in two types: general and acclimation.

c)

Psychological adaptation: It is a result of the subject’s prior experiences and is influenced by one’s socioeconomic and cultural environment. It is largely driven by expectation and perception.

It is essential to note that all three of the aforementioned adaptations occur simultaneously, making it challenging to predict how each would affect the situation separately [36,37,38]. Several adaptive comfort equations have been developed, some as part of international comfort standards [2,31,34] and some for specific climatic regions [17,21,39,40]. However, we do not know the difference and similarity in the trend of the slope of adaptive models from different countries. It is imperative to study the different literature to know how the comfort temperature is influenced by the outdoor temperature and the clothing level.

1.3. Energy Saving

In most countries, buildings account for at least 40% of total energy consumption [41]. Office buildings use a lot of energy during construction and operation, which has a negative impact on the environment as well as the health and comfort of users [42,43]. In order to provide a secure and comfortable living environment, the building is essential. The heating, ventilation, and air-conditioning (HVAC) systems use around 50% of the energy in a building to create a more comfortable indoor thermal environment [44]. Aside from thermal comfort, energy consumption is an important factor to consider in HVAC systems because it determines the system’s operating costs as well as its environmental impact [8,26,45,46]. The indoor temperature set points have the greatest influence on thermal comfort. In terms of energy demand, consumption, and occupant thermal comfort, the indoor temperature set point is the most important parameter [43,45,47,48,49,50,51,52]. If people can remain comfortable over a wider range of conditions, significant energy can be saved by relaxing thermal comfort standards [53,54]. Thus, it is imperative to study energy saving by application of adaptive models from different studies to see the potential of an indoor set point on energy saving in air-conditioning buildings.

1.4. Objectives of the Study

The main objective of this study is to analyze the adaptive thermal comfort studies conducted in office buildings across the world. Specific objectives are:

• To analyze the comfort temperature by regression equation;
• To analyze the relation of comfort temperature (seasonal differences) with indoor or outdoor temperature;
• To analyze the relation between clothing insulation and outdoor air temperature;
• To analyze the energy-saving potential by thermal adaptation.

2. Methodology

To carry out this study, the keyword “thermal comfort study in office building” is used to search the Scopus database. The present study gives detailed insight into the thermal comfort studies conducted in office buildings across the world. The following procedure has been adopted in this research.

2.1. Paper Collection

A large number of papers were collected from the Scopus database for a systematic review. Related articles were searched using keywords from the article abstract, title, and keyword list, as shown in

Table 1. Keyword used in the literature search.

Research Area	Keywords
Adaptive thermal comfort	Thermal comfort; Thermal environment; Thermal adaptation; Thermal perception; Adaptive approach; Productivity loss; Comfort temperature; Seasonal differences; Regional differences
Energy issue	Energy saving; Energy conservation; Energy performance; Adaptive control algorithm; Natural ventilation; Thermostat management; Set points

. Then, tables were created by extracting information from the research articles such as sample size, survey time, geographic location, climate, office operating style, comfort temperature, average clothing level, average outdoor temperature, etc. It was found that several research articles did not provide the seasonal comfort temperature in the text. As a result, the given figure was used to extract the comfort temperature.

The review process identified 120 papers suitable for a systematic review related to the topic. The line graph in Figure 1 displays the research that has been reviewed chronologically in this article. The considerable recent publication reveals significant importance of the topic.

2.2. Outdoor Climatic Data Collection

Outdoor temperature data were not given in some of the research papers and thus an online weather data source was used to extract the data from the studied location [55].

2.3. Mode Definition

Different studies have different modes of office, as defined below.

• Naturally Ventilated (NV): An office is designed to function in free-running (FR) mode throughout the year.
• Free running (FR): An office building which is naturally ventilated or has an HVAC system, but during the study period, either the heating (HT) or cooling (CL) systems were turned off.
• Heating, ventilation, and air conditioning (HVAC): A heating and cooling system is installed in an office, and either system was turned on during the study period.
• Mixed Mode (MM): MM building which is classified as; “concurrent” (where natural ventilation and HVAC take place simultaneously), “zoned” (when natural ventilation and HVAC occur in different zones of buildings), and “change-over” (natural ventilation and HVAC take place in the same location at various times) [20,24].

2.4. Data Analysis

The collected data from various papers were analyzed by making a table and scatter plot. This study examined the regression equations and adaptive thermal comfort models in various office operation modes. Very few studies provided adaptive thermal comfort models. The relation of comfort temperature with indoor and outdoor temperature was analyzed by making the scatter plot. The relation of comfort with the clothing level was analyzed, as clothing is a major variable for people in order to maintain their thermal comfort. Besides thermal comfort, energy consumption is an important factor to consider in HVAC systems because it determines the system’s operating costs as well as its environmental impact [8,26,45,46]. The invention of air conditioning enabled modern buildings to regulate their indoor climate regardless of the outside conditions [32]. So, the energy-saving potential through the application of the adaptive model was analyzed in various studies. Since natural ventilation is another efficient way to reduce the amount of energy used by buildings, the potential of energy saving through the application of natural ventilation was analyzed through different studies.

3. Adaptive Thermal Comfort in Office Buildings

3.1. Outdoor and Indoor Thermal Conditions

In heated (or cooled) mode, the closed skin of the structure and the functioning of the heating and cooling systems inside it decouple the indoor temperature from the exterior temperature. Whereas, in free-running mode, the indoor temperature is connected to the outdoor temperature through the building’s fabric. The comfort temperature, indoor or globe temperature, outdoor temperature, and number of respondents from different field studies from all over the world has been tabulated in

Table 2. Outdoor temperature, indoor temperature, and comfort temperature from various studies in office buildings.

Country	Cities	References	Number of Buildings	Number of Respondents	Modes	Periods	Variable for Tc	To (°C)	Tg, Ta (°C)	Tc (°C)
Japan	Sendai, Tsukuba, and Yokohama	Goto et al. [15]	6	123	CL	Summer	SET	14.2	24.7	26 ***
	Tokyo	Indraganti et al. [18]	4	416	NV	Summer	Tg	27.5	29.4	25.8
					CL	Summer	Tg	30.2	27.9	27.2
	Yokohama, Tokyo	Damiati et al. [14]	4	127	FR	Autumn	Top	23.4	26.5	25.8
					CL	Summer	Top	23.4	25.9	25.8
	Fukuoka	Mustapa et al. [57]	4	28	FR	Summer	Top	28	28.1	26.6
					CL	Summer	Top	28	26.4	26.5
	Tokyo, Yokohama	Rijal et al. [39]	13	1350	FR	Spring	Tg	13.7	24.5	24.2
						Summer	Tg	24.6	25.8	25.7
						Autumn	Tg	17.9	24.8	24.9
					CL	Spring	Tg	13.7	25.5	25.1
						Summer	Tg	24.6	26	25.5
						Autumn	Tg	17.9	25.9	25.4
					HT	Spring	Tg	13.7	24.3	24.5
						Autumn	Tg	17.9	24	24.2
						Winter	Tg	6.7	23.6	24.3
	Tokyo, Kanagawa	Takasu et al. [58]	5	503	MM	All	SET	28.6	27.8	25
					FR		SET	24	27.3	24.3
India	Chennai	Indraganti et al. [18]	13	847	NV	Summer	Tg	31.7	30.1	27.6
			13		CL	Summer	Tg	31.2	26.9	27
	Hyderabad		12	811	NV	Summer	Tg	29.2	29.4	28.1
			12		CL	Summer	Tg	30.9	26	26.1
	Chennai, Hyderabad	Indraganti et al. [17]	28	2787	NV	Summer	Tg	25.6	28.8	28
			28		CL	Summer	Tg	28.2	26.2	26.4
	Jaipur	Dhaka et al. [56]	30	1811	NV	Winter	Tg	22.3	21.8	25.6
						Autumn	Tg	31.3	29	27
						Summer	Tg	34	31.9	29.4
	Jaipur	Dhaka and Mathur [59]	19	1020	CL	Summer	Ta	32.9	25.9	27.5
	Jaipur	Kumar et al. [60]	18	2610	NV	Summer	Tg	34	31.9	30.6
						Moderate	Tg	31.3	29.3	29.5
						Winter	Tg	22.8	22.4	25.2
	Jaipur	Tewari et al. [61]	10	1554	EC	Summer	Tg	36.2	29	28.15
	Tezpur, Shillong	Singh et al. [62]	24	460	NV	Autumn	Tg	20.9	27.9	27.3 **
	Darjeeling	Thapa et al. [63]	3	34	NV	Summer	Ta	19.8	21.7	21.8
						Moderate	Ta	19.1	19.3	20.5
						Winter	Ta	13.1	16.3	17.6
	New Delhi ****	Nicol [64] *	-	-	FR	Summer	Top	33.5	-	30.1
	Calcutta ****	Rao [65]	-	-	FR	All	Top	26.4	-	26.1
	India	Manu et al. [66]	16	6330	NV	Summer	Top	33.3	29.6	25.7
						Winter	Top	19.1	22.8	23.7
					CL	Summer	Top	33.3	24.7	25.3
					HT	Winter	Top	19.1	25.5	25.1
					MM	Summer	Top	33.3	30.2	25.7
						Winter	Top	19.1	21.8	23.9
Iraq	Baghdad ****	Nicol [64] *	-	-	FR	Summer	To	33.9	-	31.2
Indonesia	Bandung	Damiati et al. [14]	3	54	FR	Summer	Top	22.5	26.7	24.7
					MM	Summer	Top	22.5	27.1	27.5
	Jakarta	Karyono et al. [67]	7	596	MM	Summer	Top	28	27.2	26.7 **
Singapore	Singapore	Damiati et al. [14]	2	14	CL	Summer	Top	26	23.2	26.4
	Singapore	de Dear et al. [68]	12	818	CL	Summer	Top	29	22.9	24.2
	Singapore ****	Webb [69]	-	-	FR	All	To	27	-	27.3
	Singapore ****	Ellis [70]	-	-	FR	All	To	27	-	26.4
China	Changsha	Wu et al. [24]	11	430	MM	Summer	Top	29.1	26.9	26.7 **
	Harbin	Wang et al. [23]	28	88	HT	Winter	Ta	19.7	25.5	19.7
	Urumqi	Guo and Wang [71]	8	577	EC	Summer	Top	36.2	29.1	27.7
Brazil	Florianopolis	Rupp et al. [21]	3	5470	NV	All	Top	19	23.4	23.4
					HT	All	Top	22	24	24.3
Spain	Seville	Martin et al. [20]	3	54	MM	Summer	Top	38	23.5	23.6
Australia	Kalgoorlie-Boulder	Cena and de Dear [13]	22	935	CL	Summer	Top	23.7	23.4	23.3
					HT	Winter	Top	14	22	20.3
	Sydney	Wong [72]	-	1267	HT	Winter	Top	12.4	-	21
					HT	Summer	Top	21.3	-	23
	Melbourne	Ballantyne et al. [73] *	4	-	HT	Winter	Top	9.45	-	20.8
					HT	Summer	Top	19.8	-	22.8
	Sydney	Hindmarsh [74]	-	-	FR	Summer	Top	21.6	-	24.2
					HT	Winter	Top	13.3	-	22.3
					HT	Autumn	Top	15.2	-	23.9
					FR	Spring	Top	19.4	-	21.4
USA	San Francisco	Schiller et al. [75]	10	304	MM	Summer	Ta	19	23.3	22.6
						Winter	Ta	15	22.8	22.0
	New York	McConnell [76]	-	-	HT	Summer	ET	22.4	-	23.7
	New York	Gagge [77]	-	-	HT	Summer	Top	22.8	-	23.9
	Minneapolis	Newton [78]	-	-	HT	Summer	ET	21.5	-	23.6
Malaysia	Kuala Lumpur	Damiati et al. [14]	4	90	CL	Summer	Top	28.5	24.4	25.6
Switzerland	Zurich	Grandjean [79] *	-	-	HT	Winter	Top	2.2	-	20.9
	Zurich, Basel, Bern	Grandjean [80]	-	-	HT	Summer	Top	17.6	-	21.3
UK	Kew	Black [12] *	-	-	HT	Autumn	Top	6.65	-	19.2
					HT	Summer	Top	17		22.2
	Graston	Humphreys and Nicol [16] *	-	-	HT	Winter	Top	3.8	-	19.9
					FR	Summer	Top	16.4	-	20.2
					HT	Autumn	Top	10.8	-	19.3
					HT	Spring	Top	15.3	-	19.7
Africa	Port Harcourt ***	Ambler [81]	-	-	FR	All	Top	25.9	-	25
Chile	Concepcion and Santiago	Trebilcock et al. [82]	19	797	MM	Winter	Top	9.2	22	21.4
				723		Spring	Top	12.8	22.5	22.3
				761		Summer	Top	18	23.4	23.6

* Humphreys [83], ** Regression and Griffiths, *** Preferred SET, **** Office and dwellings, FR: Free Running, NV: Naturally ventilated, CL: Cooling, HT: Heating, MM: Mixed mode, EC: Evaporative cooling, T_g: Globe temperature, T_op: Operative temperature, T_a: Indoor air temperature, ET: Effective temperature, Bold: Comfort temperature calculated by Griffiths’ method.

. The relation of the globe or indoor temperature and the outdoor temperature in naturally ventilated or free-running and air-conditioned office buildings is plotted in Figure 2. Studies revealed a strong correlation (R² = 0.72) between indoor or globe temperature and outdoor temperature in naturally ventilated buildings, as indicated in Figure 2; however, the correlation is much weaker in air-conditioned buildings. This indicates that outdoor temperature is likely to affect the thermal sensation and comfort temperature in NV buildings, as mentioned by Dhaka et al. [56]. Whereas, in air-conditioning buildings, the indoor climate regulates irrespective of outdoor conditions [32]. The range of outdoor temperature was 6.7 to 38 °C and indoor temperature was 21.8 to 30.2 °C in other types (HT, CL, EC, and MM) of buildings. Whereas, in FR or NV buildings, the range of outdoor temperature was 13.3 to 34 °C and the indoor temperature was 16.3 to 31.9 °C. This suggests that the mean indoor temperature in naturally ventilated buildings varies more from winter to summer. Subjects in naturally ventilated buildings are more responsive to their environments in order to maintain thermal comfort by changing their clothing level, opening windows and doors, and using fans.

3.2. Relation between Thermal Sensation and Indoor Temperature

A thermal sensation vote (TSV) based on the occupant’s subjective rating of the occupied thermal environment using the ASHRAE 7-point scale can be used to determine their thermal comfort. Many studies have used the regression method to determine the comfort or neutral temperature [56,62,75]. The point at which the thermal sensation vote is zero or four is used to determine the comfort temperature. The regression method has traditionally been used to determine the comfort temperature by relating TSV and indoor temperature. In a climate chamber study, Fanger [84] found a regression coefficient of 0.33; thus, the temperature change required to shift one thermal sensation vote (T_req) would be 3 °C (=1/0.33). Regression equations from different field studies around the world have been tabulated in

Table 3. Regression equation from different field studies in office buildings.

Country	Locations	Reference	Modes	Data	TSV Scale	Equation	R2	Treq (°C)
Japan	Tokyo	Indraganti et al. [18]	NV	Raw	±3	TSV = 0.311Tg − 7.949	0.13	3.2
			CL	Raw		TSV = 0.299Tg − 8.109	0.09	3.3
	Fukuoka	Mustapa et al. [57]	FR	Raw	±3	TSV = 0.491Top − 13.1	0.21	2.0
	Tokyo/ Yokohama	Rijal et al. [39]	FR	Raw	1–7	TSV = 0.183Tg − 0.6	0.25	5.5
			CL	Raw		TSV = 0.228Tg − 1.7	0.08	4.5
			HT	Raw		TSV = 0.168Tg − 0.3	0.08	6.0
	Tokyo/Kanagawa	Takasu et al. [58]	MM	Raw	1–7	TSV =0.13SET * + 0.66	0.048	7.7
India	Chennai/Hyderabad	Indraganti et al. [17]	NV	Raw	±3	TSV = 0.26Tg − 7.09	0.16	3.8
	Tezpur and Shillong	Singh et al. [62]	NV	Binned	±3	TSV = 0.33Tg − 8.86	0.64	3.0
	Chennai	Indraganti et al. [19]	NV	Raw	±3	TSV = 0.313Tg − 8.17	0.29	3.2
			CL	Raw		TSV = 0.111Tg − 3.029	0.01	9.0
	Hyderabad		NV	Raw	±3	TSV = 0.215Tg − 5.682	0.17	4.7
			CL	Raw		TSV = 0.194Tg − 5.103	0.08	5.2
	Jaipur	Dhaka et al. [56]	NV	Raw	±3	TSV = 0.169Ta − 4.598	0.506	6.0
	Jaipur	Dhaka and Mathur [59]	CL	Raw	±3	TSV = 0.194Ta − 5.33	0.164	5.2
	Jaipur *	Kumar et al. [60]	NV	Raw	±3	TSV = 0.149Ta − 4.06	0.55	6.7
	Jaipur	Tewari et al. [61]	EC	Raw	±3	TSV = 0.27Top − 7.63	0.23	3.7
	Darjeeling	Thapa et al. [63]	NV	Raw	±3	TSV = 0.13Top − 2.865	0.3	7.7
Southeast Asia	Southeast Asia	Nguyen et al. [85]	NV	Binned	±3	TSV = 0.41Top − 11.45	0.96	2.4
			CL	Binned		TSV = 0.24Top − 6.2	0.905	4.2
USA	San Francisco	Schiller et al. [75]	MM **	Binned	±3	TSV = 0.328ET − 7.2	-	3.0
			MM ***	Binned		TSV = 0.308ET − 7.04	-	3.2
China	Changsha	Wu et al. [24]	MM	Binned	±3	TSV = 0.18Top − 4.86	0.74	5.6
	Harbin	Wang et al. [23]	HT	Binned	±3	TSV = 0.274Ta − 5.422	0.84	3.6
	Urumqi	Guo and Wang [71]	EC	Raw	±3	TSV = 0.5643Top − 15.8	0.38	1.77
Australia	Kalgoorlie-Boulder	Cena and de Dear [13]	HT **	Binned	±3	TSV = 0.21Top − 4.28	-	4.8
			CL ***	Binned		TSV = 0.271Top − 6.29	-	3.7
Indonesia	Jakarta	Karyono [67]	MM	Raw	±3	TSV = 0.31Top − 8.38	0.42	3.2

R²: Coefficient of determination, T_req: temperature change required to shift one thermal sensation vote, FR: Free Running, NV: Naturally Ventilated, CL: Cooling, HT: Heating, MM: Mixed mode, T_g: Globe temperature (°C), T_op: Operative temperature (°C), TSV: Thermal sensation vote, T_a: Indoor air temperature, SET: Effective temperature, ET: Effective temperature, * Office and dwellings, ** Winter, *** Summer.

. The regression coefficients of 0.25 to 0.33 in NV buildings and 0.2 to 0.3 in HVAC buildings are often obtained in field surveys (Table 3), i.e., 3 to 5 °C is required to shift one thermal sensation vote. However, few field surveys have less than a 0.2 regression coefficient, i.e., greater than 5 °C is needed to shift one thermal sensation vote, which seems unreliable to calculate the comfort temperature including thermal comfort zone (center three categories). This indicates that we need to be very careful while calculating the comfort temperature by the regression method. Thus, recently many people have been adopting Griffiths’ method to calculate the comfort temperature [13,14,17,18,21,58,62]. In Table 3, many researchers have binned the data [23,24,62,85] to increase the R² value. It has been found that the value of R² of binned data is much higher than that of raw data but the regression coefficient is similar in both analyses [38,86,87,88].

3.3. Comfort Temperature from Various Field Studies

The comfort temperature from various studies has been tabulated as shown in Table 2. The temperature required for comfort is as low as 17.6 °C and as high as 31.2 °C. Humphreys [83] also found the comfort range from 17 to 30 °C in 1978. We found the range of comfort temperature in the naturally ventilated building or free-running mode is 17.6 to 31.2 °C and the range in the air-conditioned building is 20.3 to 27.5 °C. This indicates that naturally ventilated buildings experience a higher fluctuation in comfort temperature, from winter to summer, as the occupants in these buildings are more responsive to various adaptive actions.

3.4. Seasonal Differences in Comfort Temperature

Seasonal differences in comfort temperature in office buildings from different parts of the world have been listed in

Table 4. Seasonal difference in comfort temperature in an office building.

Location	Cities	Reference	Modes	Temp. for Tc (°C)	Comfort Temperature Tc (°C)				Seasonal Difference (K)
					Spring	Summer	Autumn	Winter
Japan	Tokyo, Yokohama	Rijal et al. [39]	FR	Tg	24.2	25.7	24.9	-	1.5
			CL	Tg	25.1	25.5	25.4	-	0.4
			HT	Tg	24.5	-	24.2	24.3	0.3
Australia	Kalgoorlie-Boulder	Cene and de Dear [13]	CL	Top	-	23.3	-	20.3	3
	Sydney	Wong [72]	HT	Top	-	23	-	21	2
India	Jaipur	Dhaka et al. [56]	NV	Tg	-	25.6	27	29.4	3.4
		Kumar et al. [60] *	NV	Tg	-	30.6	-	25.2	5.4
	Darjeeling	Thapa et al. [63]	NV	Ta	-	21.8	-	17.6	4.2
USA	San Francisco	Schiller et al. [75]	MM	Top	-	22	-	22.6	0.6
Chile	Concepcion and Santiago	Trebilcock et al. [82]	MM	Top	22.3	23.6	-	21.4	2.2

FR: Free Running, NV: Naturally Ventilated, CL: Cooling, HT: Heating, MM: Mixed mode, T_g: Globe temperature, T_op: Operative temperature, Bold: Comfort temperature calculated by Griffiths’ method, * offices and dwellings.

. A seasonal difference of 0.3 to 5.4 K has been recorded in different field studies. However, in dwellings, the seasonal difference is much higher than in office buildings (

Table 5. Seasonal difference in comfort temperature in dwellings.

Location	Reference	Mode	Temp. for Tc (°C)	Comfort Temperature Tc (°C)				Seasonal Difference (K)
				Spring	Summer	Autumn	Winter
Japan	Rijal et al. [89]	NV	Tg	20.7	26.1	23.6	15.6	10.5
Nepal	Rijal et al. [90]	NV	Tg	-	21.1~30.0	-	13.4~24.2	4.9 to 13.8
Pakistan	Nicol and Roaf [91]	MM	Tg	-	26.7~29.9	-	19.8~25.1	4.8 to 7.5
Iran	Heidari and Sharples [92]	NV	Ti	-	28.4	-	20.8	7.6

NV: Naturally ventilated, T_c: Comfort temperature, T_g: Globe temperature, T_i: Indoor air temperature, Bold: Comfort temperature calculated by Griffiths’ method.

) because office occupants have more constrained adaptive opportunities than in dwellings. These seasonal differences in comfort temperature indicate that there is a need for a climate-specific adaptive model in both naturally ventilated and air-conditioned buildings for energy saving.

3.5. Relation between the Comfort Temperature and Indoor Temperature

From Table 2, the relation between the globe or indoor temperature of office buildings and the comfort temperature of the occupants in naturally ventilated and air-conditioned buildings has been plotted in Figure 3. The strength of correlation between the comfort temperature and globe or indoor air temperature in the naturally ventilated building is higher than in the air-conditioned building. This indicates that occupants are more adaptive to the indoor climate in the NV building. At indoor globe or air temperatures below 24.5 °C, the comfort temperature is always higher than the indoor air temperature, and above 24.5 °C, the comfort temperature is always lower than the indoor air temperature. The relation between comfort and indoor temperature from various studies has been tabulated in

Table 6. Regression equation of comfort and indoor temperature from different field studies.

Country	Locations	Classification	Reference	N	Modes	Data	Equation	R2	S.E.
Worldwide	Worldwide	Office	This study	24	NV	Binned	Tc = 0.654Tg + 8.40	0.80	-
				36	Other types	Binned	Tc = 0.680Tg + 7.80	0.50	-
Worldwide	Worldwide	All	Humphreys et al. [93]	66,500	MM	Binned	Tc = 0.83Top + 2.56	0.92	-
Worldwide	Worldwide	All	Auliciems and de Dear [94]	39	NV	Binned	Tc = 0.73Ti + 5.41	0.84	-
Japan	Tokyo and Kanagawa	Office	Rijal et al. [38]	7295	HT and CL	Raw	Tc = 0.61Tg + 9.7	0.34	0.01
Iran	Ilam	Office	Heidari and Sharples [92]	31	NV	Binned	Tc = 0.76Ti + 5.54	0.86	-
India	Darjeeling	Office	Thapa et al. [63]	444	NV	Binned	Tc = 0.739Top + 5.73	0.75	<0.001
	All climate	Office	Manu et al. [66]	6330	NV	Binned	Tc = 0.9Top + 2.54	0.97	<0.001
					MM	Binned	Tc = 0.75Top + 6.31	0.96	<0.001
					CL and HT	Binned	Tc = 0.91Top + 2.47	0.86	<0.001

R²: Coefficient of determination, S.E.: Standard error, NV: Naturally ventilated, MM: Mixed mode, CL: Cooling, HT: Heating, T_g: Globe temperature (°C), T_op: Indoor operative temperature (°C), T_i: Indoor air temperature, N: Number of observations.

. Where the R² in all studies has a higher value except from Japan which used raw data for regression analysis.

3.6. Relation between the Comfort Temperature and Outdoor Temperature

Indoor comfort temperature and outside temperature are strongly correlated [83]. Because people adapt well in their offices using various behavioral, physiological, and psychological adaptations [3,6], the comfort temperature indoors has been found to vary with the outdoor temperature. Field thermal comfort studies have revealed that indoor comfort temperatures are a function of the mean outdoor temperature [83,94]. This means we can relate the climate and season to the indoor comfort temperature. Standards based on such a relationship would not only improve adaptive thermal comfort but would also save energy [95]. Comfortable buildings can be designed using the adaptive relationship between comfort temperature and outdoor temperature [7].

The relationship shown in Figure 4 from Table 2 can explain the difference observed between comfort temperatures in buildings in the naturally ventilated and air-conditioned modes. People indoors in a naturally ventilated building adapt to the outdoor temperature mediated by the building’s walls, operable windows, roofs, and floors. The thermostat, often set by the building manager, controls the temperature in a heated or cooled building, and its occupants adapt to this pre-selected temperature. The given Figure 4 shows that occupants of the naturally ventilated building have higher comfort temperatures and are more correlated to the outdoor temperature than in air-conditioned buildings. When we conducted regression analysis, we found the following equations.

NV or FR buildings            T_c = 0.43T_o + 14.93   (R² = 0.71)

(1)

HVAC buildings                T_c = 0.22T_o + 19.45   (R² = 0.52)

(2)

MM buildings                   T_c = 0.18T_o + 20.31   (R² = 0.54)

(3)

Other type buildings (HT, CL, EC and MM)   T_c = 0.21T_o + 19.66 (R² = 0.54)

(4)

The above Equations (1)–(4) for NV, HVAC, MM, and Other type buildings, respectively, will be helpful for the thermal design of building. We obtained the coefficient of 0.43 for the NV building, which is steeper than the ASHRAE standard. Similarly, we obtained a 0.22 coefficient for the HVAC building, which is also steeper in slope than the CIBSE guide. This might be because of a wider range of outdoor climatic variation that has been covered by this study. Whereas, in the MM building, we obtained the coefficient of 0.18, which is more similar as found by Tewari et al. [61] in a field study.

Figure 4 has a regression line for heating, ventilation, and air-conditioning (HVAC) buildings that can be used to assess the need for heating or cooling in buildings. Because the comfort indoor temperature is always higher than the outdoor mean temperature in air-conditioned buildings, it should be possible to achieve the desired indoor temperature without the use of a heating plant in many cases. In certain climates where the outdoor mean temperature exceeds 25 °C, the comfort indoor temperature is always lower than the outdoor mean temperature, so it should be possible to achieve the desired indoor temperature without using a cooling plant in many cases through careful design.

Adaptive thermal comfort models from different field studies have been shown in

Table 7. Adaptive thermal comfort model from different field studies.

Location	References	Climate	Building	Modes	Equation	R2	S.E.
Worldwide	This study	Various climates	Offices	NV	Tc = 0.43To + 14.93	0.71	-
				CL and HT	Tc = 0.216To + 19.45	0.52	-
Worldwide	ASHRAE [2]	All climates	Mostly Offices	NV	Tc = 0.31Tom + 17.8	0.70	-
Europe	CIBSE [31]	All climates	Offices	CL and HT	Tc = 0.09Trm + 22.6	-	-
	CEN [34]	All climates	Offices	FR	Tc = 0.33Trm + 18.8	0.36	-
Japan	Rijal et al. [39]	Subtropical	Offices	FR	Tc = 0.206Trm + 20.8	0.42	0.012
				CL and HT	Tc = 0.065Trm + 23.9	0.10	0.003
Southeast Asia	Nguyen et al. [85]	Humid	Mostly Offices	NV	Tc = 0.341To + 18.83	0.52	-
India	Toe et al. [40]	Hot–humid	ASHARE-based	NV	Tc = 0.57To + 13.8	0.64	-
		Hot–dry		NV	Tc = 0.58To + 13.7	0.59	-
		Moderate		NV	Tc = 0.22To + 18.6	0.09	-
	Indrganti et al. [17]	Dry	Offices	NV	Tc = 0.26Trm + 21.4	0.058	0.028
				CL	Tc = 0.15Trm + 22.1	0.026	0.014
	Dhaka and Mathur [59]	Composite	Offices	CL	Tc = 0.078To + 23.3	0.03	-
	Dhaka et al. [56]	Composite	Offices and Dwellings	NV	Tc = 0.75To + 5.37	-	-
	Tewari et al. [61]	Composite	Offices	EC	Tc = 0.22Trm + 21.45	0.06	0.02
	Thapa et al. [63]	Cold and cloudy	Offices	NV	Tc = 0.639To + 9.02	0.67	0.001
	Manu et al. [66]	All climates	Offices	NV	Tc = 0.54To + 12.83	0.81	0.001
				MM	Tc = 0.28To + 17.87	0.72	0.001
Southern Brazil	Rupp et al. [21]	Temperate	Offices	NV	Tc = 0.56To + 12.74	0.89	-
				HT	Tc = 0.09To + 22.32	0.02	-
China	Wu et al. [24]	Temperate–humid	Offices	MM	Tc = 0.01Trm + 26.9	-	-
	Guo and Wang [71]	Hot–dry	Offices	EC	Tc = 0.06Tpma + 26.17	0.368	-
	Yang et al. [97]	Temperate–humid	Offices	NV	Tc = 0.56Trm + 12.6	0.893	-
Spain	Martin et al. [20]	Mediterranean	Offices	MM	Tc = 0.2427Trm + 19.28	0.410	-
Chile	Trebilcock et al. [82]	Temperate	Offices	MM	Tc = 0.28Trm + 18.5	0.427	-

NV: Naturally Ventilated, CL: Cooling, HT: Heating, MM: Mixed mode, T_c: Comfort temperature (°C), T_rm: Running mean outdoor temperature (°C), T_o: Outdoor temperature (°C), T_outdm: Dailly mean outdoor temperature, T_pma: Prevailing mean outdoor temperature, R²: Coefficient of determination, S.E.: Standard error

and illustrated in Figure 5 and Figure 6. The length and slope of the line, respectively, show the temperatures at which they are valid and the degree to which the individuals were sensitive to a change in temperature. For this, the term “Comfort threads” was used in a previous study [96], which illustrates how the comfort temperature varies according to different ranges of outdoor air temperatures. Different countries have their own adaptive thermal comfort model, and the trend is similar. The length of the line indicates the range of comfort temperature in a particular location. Because of the different ranges of outdoor temperatures, the line has various lengths. The comfort temperature increases with an increase in outdoor temperature. However, the coefficient is different in different countries. Adaptive thermal comfort is known to be dependent on behavioral, physiological, and psychological adaptations [3,6].

3.7. Relation between Clothing Insulation and Outdoor Air Temperature

People can change personal variables to improve their thermal comfort. One major variable for people is clothing insulation. Linear regression analysis is commonly used to predict clothing insulation that varies with the outdoor air temperature [5]. The regression equation of clothing insulation from various studies has been compiled in

Table 8. Regression equations of clothing insulation and outdoor air temperatures.

Country	Reference	Modes	Equation	N	R2	S.E.	p
Japan	Goto et al. [15]	CL	Icl = −0.013To + 0.842	123	0.307	-	<0.001
	Rijal et al. [38]	FR	Icl = −0.027To + 1.2	1095	0.27	0.001	0.001
		CL and HT	lcl = −0.015To + 1.0	6102	0.35	<0.0001	<0.001
	Takasu et al. [58]	MM	Icl = −0.018To + 0.99	2722	0.384	0.0004	<0.001
	Mustapa et al. [57]	CL	Icl = −0.02To + 0.89	222	0.029	0.006	<0.05
Southeast Asia	Nguyen et al. [85]	MM	Icl = −0.0268To + 1.264	3047	0.1321	-	0.000
India	Kumar et al. [60]	NV	Icl = −0.0135To + 0.751	-	0.3	-	-
	Singh et al. [37]	NV	Icl = −0.038To + 1.454	300	0.817	-	-

I_cl: Clothing insulation (clo), T_o: Outdoor air temperature (°C), N: Number of samples, R²: Coefficient of determination, S.E.: Standard error of the regression coefficient, P: Significance level of regression coefficient.

, and these relations of clothing insulation with outdoor air temperature are illustrated in Figure 7. The clothing insulation and outdoor air temperature have a negative correlation; whereas, comfort temperature increases with an increase in temperature (see Figure 5 and Figure 6). The negative correlation demonstrates that the respondents added layers of clothes in response to a drop in temperature and removed layers in response to an increase in temperature as a measure of adaptation. Because of the adaptive nature of the occupant in naturally ventilated buildings, the slope in NV buildings are steeper than the AC buildings except in Kumar et al. [60]. Thapa et al. [63] studied in the cold climate of India and they found a much steeper slope with high clothing insulation compared to other studies. A steeper slope indicates that the subjects respond quickly to changes in outdoor environmental conditions and are thus considered as highly adaptive [56].

4. Energy Saving by Thermal Adaptation

The building is important in creating a safe and comfortable living environment. In terms of physiological adaptation, psychological adaptation, and behavioral adaptation, occupants are considered as not only passive objects in the thermal environment, but also as active factors [6,91]. Human thermal adaptability is closely related to thermal environments in which they are exposed to large amounts of energy consumed by air-conditioning systems in order to maintain tight control of room air temperatures [51]. An indoor temperature standard that varies with outdoor temperature can reduce energy use in air-conditioned buildings by reducing the difference between indoor and outdoor temperatures [91].

4.1. Energy Saving by Changing Temperature Settings Found in Various Studies

Several field studies show that occupants are willing to accept a much wider temperature range than is typically used in practice [22,23,43,91,98,99,100,101]. Furthermore, occupants with access to a personal environmental control (PEC) system can extend the acceptable ambient temperature range to as much as 18–30 °C [101]. Previous research has shown that when occupants are actively involved in the thermal control process, including thermostat use, their satisfaction increases [3,102]. Office workers have fewer opportunities to adapt to changing thermal environments than do homeowners [103]. However, even when subjected to the same thermal environments, clothing, and activity levels, occupants with varying degrees of personal control had significantly different thermal responses [54].

In general, changing the indoor temperature by 1 K can save about 10% of the energy used for heating or cooling [104].

Table 9. Energy saving by changing temperature setting found in various studies.

Country	References	Building	Strategies	Energy Saving
China	Wang et al. [23]	Classroom and office	Lowering indoor air temperature (from 25.5 to 20 °C in winter and to 22 °C in spring)	About 9.6% of energy saving from centralized heating system
	Chow and Lam [43]	Offices	Raising set point temperature (from 21.5 to 25.5 °C)	29% cooling energy saving
USA	Ghahramani et al. [99]	Office	Setting set points in the range of 22.5 ± 3 °C in small, medium, and large office buildings	Lead to 10.1–37.0%, 11.4–21.0%, and 6.8–11.3% saving, respectively
	Hoyt et al. [51]	Offices	Each degree Celsius increase or decrease in the set point	Saving is about 10% of energy
	Hoyt et al. [50]	Offices	By increasing cooling set point of 22.2 °C to 25 °C and heating set point of 21.1 °C to 20 °C	Average of 29% and 27% of total HVAC energy saving is achieved
Singapore	Sekhar [108]	Office	The space temperature is raised from 23.5 to 25.5 °C	13% of annual cooling energy saving
Malaysia	Saidur [22]	Office	Raising thermostat set point temperature from 22 to 26 °C	24% cooling energy saving
Thailand	Yamtraipat et al. [100]	Office	Raising set point temperature from 22 to 28 °C	About 6.14% energy consumption reduction per temperature set point
Australia	Roussac et al. [105]	Office	Static (raise set point temperature 1 °C higher than normal over summer)	6% reduction in energy cost
Pakistan	Nicol and Roaf [91]	Office	Raising set point temperature from 26 to 30 °C based on adaptive model	20–23% cooling energy saving

summarizes the energy-saving potential found in various studies by changing temperature settings. The results showed that by lowering the temperature setting for heating and increasing the temperature setting for cooling, significant amounts of heating and cooling energy could be saved. It is clear that significant amounts of energy saving are possible, ranging from a 37% decrease in overall energy costs in small office buildings (one floor) in the USA [99] to a 6% reduction in energy costs in Australian office buildings by lowering the 1 °C set point temperature [105]. Similar review conducted by Rijal et al. [106] and a literature analysis conducted by Yang et al. [107] also support these findings. Additionally, by using a strong passive building design and permitting different adaptation measures, we can reduce the energy used for heating and cooling.

4.2. Energy Saving by Natural Ventilation and Adaptive Model in Various Studies

Natural ventilation refers to a system in which an occupied space’s interior air is continuously replenished by the relatively fresh exterior air through vents, windows, doors, and other openings [109]. Natural ventilation is a good way to reduce the amount of energy used by buildings. Natural ventilation has a tremendous potential to lower energy consumption and the cost of the HVAC system by supplying and removing air from an indoor space without the usage of mechanical devices [110,111]. Advanced NV technologies, such as wind towers, solar chimneys, and automated window controls, are already receiving a lot of attention in Europe and North America [6,112,113], and they show a significant reduction in cooling energy usage of up to 40–50% in some places [114]. Natural ventilation does not require a mechanical system and does not use electricity [115]. Natural ventilation can be used to reduce building energy consumption, achieve thermal comfort, and maintain a healthy indoor environment [116].

Table 10. Energy saving by natural ventilation and adaptive model in various studies.

Country	References	Building	Strategies	Energy Saving
Spain	Barbadilla-Martin and Martin [117]	Office	Adaptive control algorithm	Energy saving of 27.5% and 11.4% for cooling and heating periods, respectively
China	Tong et al. [111]	Office	Natural ventilation	Natural ventilation can save 8–78% of cooling energy, depending on the local climate
	Wu et al. [24]	Office	Adaptive comfort temperature zone	Summer cooling energy savings of 8.6%
South Korea	Yun et al. [118]	Office	Adaptive comfort models of air-conditioned buildings	The adaptive comfort control saves 22% of daily cooling energy
Belgium	Gratia and Herde [114]	Office	South-facing double-skin facade	Reduces cooling loads by 20.5%

summarizes the energy-saving potential by natural ventilation and adaptive models in various studies. By implementing adaptive models in air-conditioned buildings, significant amounts of heating and cooling energy can be saved. It has been observed that up to 27.5% of cooling energy has been saved by an adaptive control algorithm, and up to 78% of cooling energy has been saved through the natural ventilation, having an impact on climate and ambient air quality. Adaptive models would be important control strategies for energy-saving building designs because of different adaptive measures in adaptive models such as window opening and clothing adjustments.

5. Overall Discussion

The indoor operative temperature at which the average subject votes neutral on the thermal sensation scale is referred to as the comfort temperature. As shown in Figure 3, studies revealed a strong correlation (R² = 0.72) between indoor or globe temperature and outdoor temperature in naturally ventilated buildings; whereas, the correlation between indoor or globe temperature and outdoor temperature in an air-conditioned building is much weaker (R² = 0.37). This suggests that outdoor temperature is likely to affect thermal sensation and comfort temperature, and that indoor temperature fluctuates more from winter to summer in NV buildings, as mentioned by Dhaka et al. [56].

The adaptive approach to thermal comfort [7] was developed based on the results of field studies of thermal comfort. Previously, the regression method was used in many studies to calculate comfort temperatures. Few field surveys [24,39,56,58,60] have less than a 0.2 regression coefficient, i.e., greater than 5 °C is needed to shift one thermal sensation vote. More than 5 °C to shift one thermal sensation vote seems impractical. The linear regression approach can sometimes provide an extraneous number when the TSV is located away from the neutral point, as previously noted by Thapa et al. [63] and Indraganti [119]. Therefore, we need to be careful while calculating comfort temperature from regression analysis. The Griffiths’ method, which has been used by various researchers [14,18,19,39,57,60,63,104], is another method for calculating the comfort temperature.

Adaptive thermal comfort in office buildings studies from around the world revealed that comfort temperatures [60] in tropical areas were higher than those in cold climatic zones, and comfort temperatures in winter were lower than in summer in the same region [13,23,39,56,60,75], indicating that people might adapt to the climate. The temperature required for comfort can range between 17.6 °C and 31.2 °C. Humphreys [83] also found the comfort range to be from 17 °C to 30 °C in 1978. However, different field studies found that the seasonable difference in comfort temperature in an office building is lower (0.3–5.4 K) than in dwellings (4.9–13.8 K) (Table 4 and Table 5).

Furthermore, as discovered by [38,92,93], there is a strong relationship between comfort temperature and indoor air or operative temperature. A naturally ventilated building has a stronger correlation than an air-conditioned building. Because people adapt well in their offices using various behavioral, physiological, and psychological mechanisms, the comfort temperature indoors was found to vary with the outdoor temperature [3,6].

Clothing is a major variable for people in order to maintain their thermal comfort. The clothing level always varies with the outdoor temperature. Because of different adaptive measures in naturally ventilated buildings, the correlation of clothing insulation of occupants to the outdoor air temperature is higher than in air-conditioned buildings.

According to various thermal comfort field studies, indoor comfort temperature is related to outdoor climate. As a result, standards based on this relationship would not only improve comfort but would also result in energy saving [95]. Data from several large field studies (Table 8) show that occupants are willing to accept a much wider temperature range than is typically used in practice [22,23,43,91,98,99,100]. Furthermore, natural ventilation has the potential to significantly reduce energy consumption and the cost of the HVAC system, which supplies and removes air from an indoor space without the use of mechanical systems [110,111].

In addition to thermal comfort, energy consumption in HVAC systems is vital to consider. Human thermal adaptation is directly connected to thermal environments where people are subjected to high energy consumption from air-conditioning systems to maintain tight control over the temperature of the room [51]. Besides thermal adaptation, the recent studies [120,121] show that energy saving from accurate occupancy detection by implementing artificial intelligence in the building also has a significant role.

Comfort temperatures proposed by different studies from naturally ventilated or free-running buildings are plotted on the ASHRAE 55 comfort band, as shown in Figure 8 from Table 2. The regression line shows that the slope of this study is steeper as it has a higher coefficient than in ASHRAE 55. This might be because of a wider range of outdoor climatic variation that has been covered by this study. It has been observed that most of the comfort temperature lies within the ASHRAE comfort band, and comfort temperature is increasing with the increase in outdoor temperature. As we know, the adaptive behavior is inherent in culture and climate.

Comfort temperatures proposed by different studies from air-conditioned buildings are plotted on the CIBSE guide, as shown in Figure 9 from Table 2. The regression line shows that the slope of this study is steeper as it has a higher coefficient than in the CIBSE guide. This might be because the CIBSE guide was derived from the European context while this study covers all studies conducted in the world. Therefore, it has been observed that more than one-third of the comfort temperature is outside the CIBSE comfort band. Figure 9 clearly shows that the equation derived from the CIBSE guide in air-conditioned buildings might not be applicable in the other context.

6. Conclusions

This study on adaptive thermal comfort in office buildings based on a literature review found the following conclusions:

• Studies revealed a strong correlation between indoor or globe temperature and outdoor temperature in naturally ventilated buildings. Whereas, the correlation is much weaker in air-conditioned buildings.
• While calculating comfort temperature through the regression method we have to be careful as it may require more than 5 °C to shift one thermal sensation vote, which is inappropriate.
• The temperature required for comfort is as low as 17.6 °C and as high as 31.2 °C.
• Different field studies found that the seasonable difference in comfort temperature in office buildings is 0.3–5.4 K, which is much lower than in dwellings.
• A strong relation of comfort temperature and indoor temperature was observed from various field studies.
• The new adaptive thermal comfort Equations (1)–(4) were proposed on the basis of different field studies for NV, HVAC, MM, and other types of office buildings, which will be helpful for the thermal design of buildings.
• The correlation of clothing insulation of occupants to the outdoor air temperature in naturally ventilated buildings is higher than in air-conditioned buildings.
• Various studies show that substantial amounts of energy can be saved by changing the set point and natural ventilation. It was observed that up to 37% of cooling energy was saved by raising set point temperature based on an adaptive model. In addition, up to 27.5% of cooling energy was saved by the adaptive control algorithm and up to 78% cooling energy was saved through natural ventilation.

This study was conducted covering all climate through a single glass. The most significant limitation of field studies is the difficulty in obtaining reliable data. Because it is difficult to cover all aspects of comfort in a single study, this limitation can be overcome by increasing the number of studies and thus increasing the possibility of covering the most aspects of thermal comfort. These variations may be due to different climate types and dynamic natures, such as different behavioral patterns, study periods, number of observations, methods of analysis, and indices used. Adaptive thermal comfort standards based on climatic adaptations for desired indoor design temperatures will pave the way for energy-efficient design development. Additionally, this study successfully demonstrates the necessity for climate-specific thermal comfort standards for the development of energy-efficient designs, as existing comfort standards might not be applicable in all regions.