
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12359-6
10.1016/j.heliyon.2024.e36328
e36328
Research Article
The influence of occupant control behaviour on thermal comfort under different heating charging modes in residential buildings in cold regions
Yan Haiyan yhy@hpu.edu.cn
ab⁎
Xie Linxiao a
Chow David c
Chen Xu d
Li Meng d
a School of Architectural and Artistic Design, Henan Polytechnic University, Jiaozuo, Henan, 454000, China
b Engineering Laboratory of Ecological Architecture and Environment of Henan Province, Henan Polytechnic University, Jiaozuo, Henan, 454000, China
c School of Architecture, University of Liverpool, L69 7ZN, Liverpool, UK
d School of Civil Engineering, Henan Polytechnic University, Jiaozuo, Henan, 454000, China
⁎ Corresponding author. School of Architectural and Artistic Design, Henan Polytechnic University, Jiaozuo, Henan, 454000, China. yhy@hpu.edu.cn
14 8 2024
30 8 2024
14 8 2024
10 16 e363288 2 2024
9 8 2024
13 8 2024
© 2024 Published by Elsevier Ltd.
2024

https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
In China's central heating, there are two modes for calculating heating costs, which are divided into Charging by flow mode which charges according to the amount of use and Charging by area mode which charges according to the floor area. The Charging by flow mode has been increasingly adopted by numerous urban central heating buildings. Thus it is worth investigating whether occupants experience varying levels of thermal comfort under these two modes. To address this, a field test and subjective questionnaire survey were conducted on residential buildings in cold regions of China during the heating season. The study assessed 134 residential occupants utilizing radiator heating, comprising 66 in Charging by area and 68 in Charging by flow modes. A collection of 1206 valid data points was obtained, with 609 in Charging by area mode and 597 in Charging by flow mode. The findings reveal noteworthy disparities in the duration, area, and strength of heating equipment usage between the two modes. While there are no marked variances in the interior and exterior environmental conditions under both modes, residents in the Charging by flow mode experience enhanced thermal comfort, acceptability and expectation, as well as better air quality satisfaction. Perceived control can greatly enhance individuals' thermal sensation in temperatures below 18 °C and above 24 °C. The impact of perceived control on thermal expectation is linear with temperature adjustments. The heightened degree of sensing control in Charging by flow mode lowers residents' expectations of high temperatures, broadens the range of acceptable low temperatures and accomplishes energy conservation and carbon reduction while ensuring optimal comfort.

Keywords

Heating charging mode
Thermal response
Central heating
Winter
Control behavior
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pmc1 Introduction

Buildings consume a significant proportion of energy, accounting for approximately 30 % of all human activity [1]. Energy consumption in China is growing at an annual rate of over 7 % [2], representing three-quarters of global net growth. According to current trends, it is anticipated that China's building energy consumption will reach 40 % by 2030 [3]. Concurrently, with the rapid development of China's social economy, people's winter heating needs are increasing [4]. Most individuals spend 80 %–90 % of their time indoors daily in the winter, making indoor thermal comfort of utmost importance. However, it is also regarded as one of China's primary contributors to carbon emissions and energy consumption. Currently, China has two forms of heating charge mode: CBA mode and CBF mode. The main distinction between the two modes arises from the varying approaches and levels of environmental occupant heat control behaviour attributed to the different charging methods. The cost of heating stands as a pivotal factor for most individuals, with the calculation of heating expenses strongly influencing heating system preferences. In fact, many occupants are willing to compromise on heating comfort to curtail heating costs. Therefore, it is crucial to analyze how various modes of charging heating costs affect the subjective response of occupants to their heating behavior. This examination will enable us to diminish carbon emissions from heated buildings [5].

1.1 Influence of control behavior on thermal comfort

Control behavior generally refers to the ability and way that people can change the ambient temperature and humidity in a specific thermal environment [6]. Recent research [7] has also shown that increased personal control can also significantly reduce energy consumption [8,9].

First of all, the influence of control behavior on thermal comfort is reflected in many aspects. Nicol [10] proposed the theory of adaptive thermal comfort in 1973. According to the experiment, the subjects achieved thermal comfort by changing their clothing, metabolic rate, and the thermal environment. Behavioral adaptation in thermal adaptation theory involves control strategies like opening or closing windows and adjusting heating [11]. When occupants have greater control, they perceive more opportunities to adjust to their surroundings, resulting in a reduced likelihood of complaints [12]. Additionally, occupant behaviour plays a crucial role in achieving optimal thermal comfort and energy efficiency in a building.Bzy et al. [13] investigated the artificial laboratory's air-conditioned environment and discovered that increasing the degree of control would lower the likelihood and risk of subjects' discomfort with the surroundings, even though varying control strategies did not have an impact on the indoor temperature. Gucyeter [14] examined the effects of three distinct behaviour modes in office buildings and demonstrated that each mode interacts with heating and lighting systems in diverse ways. Consequently, occupants must control how buildings function. The impact of occupant control behaviour on thermal performance has been a topic of research for an extended period. Research by Deuble and de Dear [15] on two office buildings in Sydney discovered that individuals with greater intentions to control the environment had a higher thermal tolerance. In addition to examining the variation in occupant thermal response under different heating methods, including radiators and underfloor heating, this study also reveals that the choice of equipment control method affects people's thermal response. For instance, Yun [16] conducted field measurements in seven air-conditioned buildings with operable windows during the summer season. It was discovered that during the summer, the optimum temperature for the group with strong thermal sensation and control was 0.9 °C higher compared to that of the group with weak thermal sensation and control. Furthermore, based on simulated energy consumption calculations, the energy consumption within this environment could be decreased by 9 %. When analyzing the thermal perception variance between centralized and split air conditioning within an air-conditioned environment, it was discovered that the acceptable range of indoor temperatures is more limited for those using centralized air conditioning.

The research findings of previous scholars demonstrate that people's thermal environment needs are influenced by various factors, beyond objective environmental parameters. Apart from heating system and its control mode causing variations in heat demand, there is a question of whether different heating charging modes also impact on individuals' thermal responses.

1.2 Control behavior and energy consumption of different devices

In addition to increased comfort, higher control will also lead to a corresponding reduction in heating energy consumption [17,18]. Xu and Li [19] conducted a study on residential buildings and discovered that a deviation from 18 °C in indoor operative temperature resulted in occupants with higher control intentions experiencing a neutral thermal sensation. In this case, the level of energy consumption also decreases significantly. Similarly, Nakaya et al. [20] found that different control behaviors have an impact on each other, such as the presence of a fan in a room causing changes in the habit of opening and closing windows, The combination of multiple control methods will also increase the degree of perceived control, thereby reducing heating energy consumption [21]. Yun [16] conducted field measurements in seven air-conditioned buildings with operable windows during the summer season. It was discovered that during the summer, the optimum temperature for the group with strong thermal sensation and control was 0.9 °C higher compared to that of the group with weak thermal sensation and control. Furthermore, based on simulated energy consumption calculations, the energy consumption within this environment could be decreased by 9 %. When analyzing the thermal perception variance between centralized and split air conditioning within an air-conditioned environment, it was discovered that the acceptable range of indoor temperatures is more limited for those using centralized air conditioning.

Thus, it prompts the question of whether different heating charging modes would affect occupants’ control behaviour. The research indicates that a higher degree of thermal control and increased ventilation lead to a better thermal response in individuals, but this improvement is only effective within a certain temperature range. Nonetheless, no quantitative study exists on how different heating charging modes alter the impact of thermal control behaviour on each thermal response.

However, most of the existing studies focus on the thermal comfort of the physical differences of different heating ends, and most of the existing studies focus on the physical differences of different heating ends. there is a limited amount of studies investigating the thermal environment and response of occupants under varied heating charging modes.

1.3 Objectives of this study

China's heating modes have diversified in response to the varied heat demands of the population. The indoor heating systems have evolved from the conventional radiator to the low-temperature radiant underfloor heating system. A range of heating techniques are available to fulfil diverse indoor heating requirements. Scholars in China and overseas have researched the impact of various indoor heating modes on the thermal environment and thermal response. Underfloor heating and radiators are the two most frequently used heating systems in China. Previous research has primarily concentrated on comparing heating methods. Zeiler and Boxem [22] conducted a survey evaluating the thermal comfort of students in educational buildings using both underfloor heating and radiator heating. The results demonstrated that individuals preferred floor heating marginally over radiator heating. Chung et al.'s [23] research confirmed this conclusion and additionally identified that variations in thermal comfort could originate from thermal stratification and thermal asymmetry, triggered by radiant heating. The study [24,25] literature states that radiant heating provides superior thermal comfort due to its ability to create a smaller vertical air gradient, lower wind sensitivity, and a “cold head and warm feet” thermal environment, which improves blood flow.

Based on previous studies, the following issues currently exist: firstly, most studies concentrate on distinct thermal surroundings and comfort regarding various heating equipment and devices whereas the comparison between various charging methods is still unknown. Secondly, the effect of different heating charging schemes on residential heat control conduct in central heating buildings has not been thoroughly examined. Finally, the specific quantitative influence of heat control behaviour on people's subjective reaction in central heating buildings also remains inconclusive. It is unclear whether control behaviour has the same impact on subjective responses. Therefore, it is worth exploring the distinctions and rationales for thermal comfort under various modes of heating charging.

This study examines a residential building in the Jiaozuo region of China, using a combination of objective environmental parameter measurements and subjective human body investigations to analyze indoor and outdoor thermal conditions and occupant thermal responses when subjected to different heating charging modes. The primary research objectives are as follows:1) Investigate the impact of various heating charging modes on occupant thermal response and satisfaction with air quality.

2) Quantitatively study the influence of occupant thermal control behaviour on individuals' thermal response within different heating temperature segments.

3) Analyze and compare the heating stability of Charge by area mode and Charging by flow mode, and the thermal response stability of occupants.

2 Methodology

2.1 Location and climate

Jiaozuo is situated in the chilly climatic region of central China, the climate in the Koeppen-Geiger zone is warm and dry in winter. With the heating season spanning from October to March of the following year, significantly escalating heating energy consumption [26].

Fig. 1 shows the variation of outdoor monthly temperature in the heating season. The current charging system for heating in Jiaozuo comprises two main modes: charging by building area (CBA) and charging by actual heating flow (CBF).Fig. 1 Outdoor monthly temperature variation.

Fig. 1

2.2 Building and respondents

The thermal environment indoors and the comfort level of residents in the residential areas surveyed were tested on-site (Fig. 2). The survey was carried out in the heating seasons of 2018 and 2020, respectively. During the survey, there were two primary types of radiator terminals used: radiators and underfloor heating. To remove any differences in the impacts of heating terminals on the environment and people's thermal response, only occupants with radiator terminals were chosen for analysis. 134 occupants using radiator heating terminals (as shown in Fig. 3) were surveyed. Of these, 66 occupants were charged according to their home's area (CBA) and 68 occupants were charged based on flow (CBF).Fig. 2 The main structural form of the residential building in Jiaozuo City.

Fig. 2

Fig. 3 The main forms of windows and split air conditioners.

Fig. 3

Residential properties were randomly selected as the research subjects. The selection of residential samples took into consideration the impact of the residential structure, architectural design, orientation, windows, balconies, ventilation, heating, and other variables on the indoor thermal environment and human thermal response. No significant differences were observed in the external structure of the two charging methods selected.

All participants in the survey were healthy and had resided in the study area for at least one year, becoming acclimatized to the local conditions. To mitigate the impact of child subjects on overall data stability, the minimum age of subjects was 13 years. Before completing the questionnaire, participants were instructed to refrain from strenuous physical activity for at least an hour prior and to maintain a maximum metabolic rate of 2 to avoid any impact on their thermal comfort. Table 1 provides a summary of their information.Table 1 Subjects’ background statistics.

Table 1Charging mode		Age	Height/cm	Weight/kg	Clo	Metabolic rate/met	
CBA mode	Mean	39	166	64	1.2	1.2	
Max	77	186	90	2.4	2.0	
Min	13	150	45	0.4	0.8	
Std	10.3	8.2	11.5	0.3	0.1	
CBF mode	Mean	41	165	62	1.2	1.2	
Max	80	190	94	2.4	2.0	
Min	13	150	45	0.5	0.8	
Std	9.0	8.6	11.6	0.3	0.1	

2.3 Fees and charging methods for different charging modes

Currently, the majority of residential buildings in most areas utilize the CBA mode, whereby the heating charge is paid as a single lump sum at the commencement of the heating season. However, there has been a growing preference for the CBF mode in recent years, which is based on the actual heating flow used by occupants. CBF mode affords residents the opportunity to adjust the flow valve opening degree as per their personal requirements. Yu et al. [27] discovered that occupants utilizing CBA mode more commonly adjust by opening windows or wearing less clothing to adapt to their surroundings, rather than powering off heating equipment. This quells comfort and results in needless energy waste throughout the heating season [28]. The economic variance in heating charging modes appears to result in a psychological deficit among occupant residents in certain areas. Empirical research conducted by Zhao et al. [29] among rural residents indicates that heating costs lead to declines in occupant heating demand. Additionally, disparities in heating costs and charging methods can impact people's thermal comfort and heat usage behaviour.

The study gathered and analyzed annual heating expenses for occupants employing varying charging modes. The findings, displayed in Fig. 4, indicate that CBA mode users incurred annual heating costs primarily within the 2000–3000 and over 3000 yuan ranges, while CBF mode users experienced costs concentrated in the 1000–2000 yuan range. To ascertain the impact that surface area may have had on overall heating costs, floor area measurements were also acquired and they are reported in Fig. 5. The study reveals that the size of dwellings in CBF mode occupants is predominantly in the range of 120–150 sq.m. Conversely, homes using CBA mode are substantially smaller, being less than 90 sq.m or over 150 sq.m. Given the complete survey of both building area and structure detailed in Section 2.2, overall trends show no significant divergence in building area between occupants utilizing the two charging modes (P > 0.05). To elucidate the contrast in heating expenses between the two charging modes, the annual heating expenditure of each occupant was meticulously compared to the living area during subsequent analysis. The calculated yearly unit area heating cost of the CBA and CBF models are 1.33 yuan/m2 and 1.20 yuan/m2, respectively, as illustrated in Fig. 6. The data shows that the CBA model outperforms the CBF model in both the total range of heating costs and the heating costs per unit area.Fig. 4 The difference in heating cost between CBA mode and CBF mode occupants.

(ns represents P > 0.05, * represents P < 0.05, ** represents P < 0.001).

Fig. 4

Fig. 5 The difference in Floor area between CBA mode and CBF mode occupants.

(ns represents P > 0.05, * represents P < 0.05, ** represents P < 0.001).

Fig. 5

Fig. 6 Heating cost variation per unit area.

(ns represents P > 0.05, * represents P < 0.05, ** represents P < 0.001).

Fig. 6

2.4 Field investigation and instruments

2.4.1 Questionnaire survey

The combination of environmental parameters and subjective responses is the measurement of environmental parameters at the same time of questionnaire survey, which is one-to-one correspondence. The questionnaire includes basic information of the subjects (age, height, weight, clothing, activity level, etc.) and subjective responses (thermal sensation, thermal expectation, thermal comfort, thermal acceptability, air quality satisfaction).

The thermal sensation voting uses a seven-point scale in the ASHRAE standard 55–2020(Thermal Environmental Conditions for Human Oecupancy) [30]. In addition, the study uses a six-point scale for thermal comfort [31], a four-point scale for thermal acceptability [32,33], and a three-point scale for thermal expectation [34,35]. The air quality satisfaction voting uses a seven-point scale. The voting scales used in the survey are shown in Table 2.Table 2 Subjective thermal response voting scale.

Table 2Voting scale	Thermal sensation	Air quality satisfaction	Thermal comfort	Thermal acceptability	Thermal preference	
−3	Cold	Very dissatisfied	Very uncomfortable			
−2	Cool	Dissatisfied	Uncomfortable	Totally unacceptable		
−1	Slightly cool	Slightly dissatisfied	Slightly uncomfortable	Barely unacceptable	Cooler	
0	Neutral	Neutral			No change	
+1	Slightly warm	Slightly satisfaction	Slightly comfortable	Barely acceptable	Warmer	
+2	Warm	satisfaction	Comfortable	Totally acceptable		
+3	Hot	Very satisfaction	Very comfortable			

In order to explore people's use of heating equipment or valves in the two modes, a survey of thermal control behavior have been added to the questionnaire. For example, residents have questions about the opening degree of heating valves, the daily heating time, the selection of heating rooms, and the opening and closing of Windows. Among them, the time, space and intensity of the use of heating equipment are respectively investigated by three questions in the questionnaire: “the use time of equipment”, “the selection of heating room” and “the opening degree of heating valve”. The device use time problem is set to 0∼24 h different options for residents to choose; The choice of heating room, the problem is set for all rooms, kitchen, living room, study and other different rooms to choose. The problem of the heating valve opening degree is set to less than 1/2, 1/2, greater than 1/2, fully open, four choices.

2.4.2 Instruments

While the subjects were filling out the questionnaire, environmental parameters were measured (indoor and outdoor air temperature, relative humidity, air flow velocity, radiation temperature, etc.). The measuring instruments met the requirement of ISO 7726 (Analytical determination and interpretation of thermal comfort using calculation of the PMV and PPD indices and local thermal comfort criteria) [36].

The height of measuring points was 0.6 m for sitting position and 1.1 m for standing position. 0.6m height is used when we ask the subjects to adopt a sitting posture; 1.1m height is used when the subject is in a standing position.Due to the limitation of manpower and instruments, some occupants were selected to test indoor air quality parameters CO2 and PM2.5, and a total of 156 sets of samples were obtained. The instruments for measuring environmental parameters are shown in Table 3.Table 3 Measured range and accuracy of the instrument.

Table 3Instrument	Test content	Measuring range	Measure precision	
PC-4 Automatic meteorological station	Tout	−40∼70 °C	±0.1 °C	
RH	0 %–100 %	±5 %	
JT-IAQ Indoor thermal comfort tester	Ta	1∼60 °C	±0.3 °C	
RH	10 %–98 %	±1.5 %	
Va	0.05–5 m/s	±(0.03 m/s+2 % reading)	
Tg	1∼60 °C	±0.3 °C	
TR-72U Self-recording thermometer and hygrometer	Tout	−10∼+60 °C	±0.3 °C	
RH	0 %–100 %	±5 %	
QD-M1 A variety of pollutant monitoring equipment	PM2.5	0∼500 μg/m3	±1 μg/m3	
CO2	0∼5000 ppm	±1 ppm	

2.5 Statistical analysis

Air temperature (ta) and mean radiant temperature (tr) both affect human thermal sensation, therefore, operative temperature (to) is used as the evaluation index of thermal comfort in this study. In data analysis, the operative temperature was divided into bins according to 0.5 °C, and its average value was used as a variable for each bin. Independent sample T-test was used to compare the differences between different bins of data. Weighted linear or polynomial regression analysis was performed on the relevant factors affecting thermal sensation, and covariance analysis was used to test the significant difference of slope and intercept among regression lines. All data analysis was carried out using SPSS V 26.0 software. The P-value (the probability that the difference between samples is caused by sampling error) was used to describe the significant difference between the two groups of data. When the difference was significant, P < 0.05, while P > 0.05 meant that the difference was not significant.The confidence interval for the data is 95 %.

3 Results

3.1 Thermal environments

The statistics of indoor and outdoor environmental parameters of the two charging modes are shown in Table 4. Comparing the indoor and outdoor environmental parameters of the two charging modes, it is found that there is no significant difference in the outdoor air temperature, indoor operative temperature, wind speed, indoor and outdoor relative humidity and CO2/PM2.5 content in a statistical sense (P > 0.05). Therefore, there is no significant difference in the indoor thermal environment between the two modes.Table 4 Distribution of indoor and outdoor environment parameters.

Table 4Heating charging mode	Outdoor	Indoor	
Tout/°C	RH/%	CO2/PM2.5(ppm/μg/m3)	To/°C	RH/%	Va/(m/s)	
CBA mode (49.3 %)	Mean	32.6	60.6	826.4/74.4	21.1	40.7	0.01	
Max	41.1	86.2	1305/105	26.4	79.0	0.18	
Min	22.5	18.9	376/11	12.6	13.0	0.01	
Std	4.0	17.4	3.5/3.1	2.4	12.2	0.02	
CBF mode (50.7 %)	Mean	31.5	60.6	865.5/70.1	20.9	38.8	0.02	
Max	41.1	86.2	1413/99	26.8	74.8	0.30	
Min	21.8	18.9	385/13	13.1	15.5	0.01	
Std	4.2	17.4	4.1/2.9	2.5	9.6	0.06	

3.2 Subjective responses

The analysis carried out in Section 3.2 indicates that there is no significant distinction in the indoor thermal environment between CBA and CBF modes. But whether there is consistency in the thermal response of residents in the two modes. After comparing and analyzing the subjective response of residents in CBA and CBF modes, it was revealed that the subjective response of residents in CBF mode was superior. The independent sample T test was used to test the differences between CBF and CBA modes.After comparing and analyzing the average thermal response of the two models, it is found that the thermal sensation, thermal comfort, thermal acceptability, and air quality satisfaction of CBF residents were significantly higher than those of CBA residents(P < 0.005).thermal expectation. The thermal expectation of the two models showed that the high temperature expectation of the households in the flow mode was lower.

After comparing the average thermal response, we continue to use the method of independent sample T test to analyze the difference of the mean of each voting option for a single subjective response. As depicted in Fig. 7 (a), the proportion of residents who voted for 0 or 1 (neutral and warm) in CBF mode was considerably higher than that in CBA mode. As shown in Fig. 7 (b), the study found a significant difference in the percentage of residents voting for −2 and −1 (indicating discomfort and slight discomfort) in CBA mode compared to CBF mode (P < 0.005). Furthermore, the percentage of residents voting for 0 (indicating a neutral level of comfort) in CBF mode was significantly higher than in CBA mode. As demonstrated in Fig. 7 (c), the percentage of residents who voted for a thermal acceptability rating of just acceptable was considerably higher in comparison to those in CBA mode (P < 0.05).Fig. 7 Distribution frequency of subjective responses. (ns represents P > 0.05, * represents P < 0.05, ** represents P < 0.001).

Fig. 7

Furthermore, the proportion of residents who voted for unacceptable, just unacceptable, and general thermal acceptability ratings (−2, −1, and 0) was higher in CBA mode compared to CBF mode. According to the results presented in Fig. 7 (d), residents’ thermal expectations in CBF mode leaned towards (no change) and fewer residents anticipated warming or cooling (P < 0.05). The differences in air quality satisfaction votes are shown in Fig. 7 (e). The percentage of CBF occupants in terms of 1 and 2 is significantly higher than that of CBA occupants (P < 0.005). Therefore, on the premise of consistent indoor environmental parameters, compared with CBA mode residents, CBF mode users have more warm thermal feeling, lower discomfort ratings, more users expect the same environmental parameters, and higher acceptability and air quality satisfaction.

3.3 Neutral temperature, preferred temperature and acceptable temperature

Grouping was conducted at 0.5 °C intervals, and linear regression analysis was performed on thermal sensation vote TSV and operative temperature to. The results are shown in Fig. 8, and the equations are shown in (1), (2).(1) CBA mode: TSV = 0.174 to - 3.423(R2 = 0.805, P < 0.001)

(2) CBF mode: TSV = 0.104 to - 1.963 (R2 = 0.609, P < 0.001)

Fig. 8 Relationship between thermal acceptance and operative temperature.

Fig. 8

The differences between the regression equations of CBA and CBF modes were tested, and significant differences were found in slope and intercept (slope: P = 0.02, intercept: P < 0.001), as shown in Fig. 8. The confidence interval of the two curves in the figure is −0.056∼-0.014 respectively, which is within the range of 95 % confidence interval. The occupants in CBA mode were more sensitive to changes in operative temperature (0.174/K) than in CBF mode (0.104/K). The thermal neutral temperature of occupants in CBA and CBF modes was calculated by regression equations, which were 19.7 °C and 18.8 °C respectively, and the CBA mode was about 0.8 °C higher than the CBF mode.

The percentages of residents who voted for “cooling” and “warming” expectations using the two charging modes were fitted, as presented in Fig. 9.Fig. 9 Thermal expectation difference between CBA mode and CBF mode occupants.

Fig. 9

The intersection of the two fitting curves indicates the thermal expectation temperature. By calculations, the expected temperature for CBA residents is 23.6 °C and for CBF residents, it is 22.5 °C. It is evident that the CBF mode decreases people's expectations for higher temperatures.

The “just acceptable” and “completely acceptable” votes in Thermal Acceptability (TA) indicate subject acceptance of the environment. The percentage of acceptable votes in all votes within each 0.5 °C operative temperature range is analyzed, and binomial regression is performed using the corresponding operative temperature. The lower limit of the acceptable temperature for 80 % of occupants is determined by the point at which the curve intersects the 80 % acceptable percentage line, as illustrated in Fig. 10.The confidence interval is 85 %. The binomial regression equations (3), (4) reveal these findings.(3) CBA mode：TA = - 0.119 to2 + 6.468 to + 8.436 （R2 = 0.264，P ＜ 0.01）

(4) CBF mode：TA = - 0.048 to2 + 3.323 to + 47.123 （R2 = 0.333，P ＜ 0.01）

Fig. 10 The percentage of occupants acceptable to the thermal environment.

Fig. 10

TA is acceptable percentage of heat; to is operative temperature.

The confidence interval of the two curves in the figure is −0.041∼-0.012 respectively, which is within the range of 95 % confidence interval. The thermal environment tolerates temperatures of 15.5 °C and 12.0 °C for 80 % of occupants in the area and flow charging mode. occupants in flow charging mode have a lower acceptable temperature. In summary, in conjunction with the variances in subjective responses described in section 3.4, the CBF model enhances individuals’ capacity to withstand cooler indoor temperatures whilst lowering the demand for higher temperature heating environments. Moreover, this supports the hypothesis that individuals exhibiting higher levels of personal control are more amenable to a broader range of indoor thermal environments [37].

3.4 The heating behaviour in different heating charging modes

The primary discrepancy between the two charging modes is the variance in heating costs and charging methods. Accordingly, it is important to identify the influencing factors that illustrate the difference in control behaviour? Research conducted by Cao et al. [38] highlights that subjects who used personalized heating equipment such as wall-mounted furnaces and floor heating had improved thermal acceptability in comparison to those reliant on centralized heating in urban areas. This implies that residents' thermal response is enhanced when they can adjust individually to the thermal environment. The process of heat adaptation comprises psychological, physiological, and behavioral factors. Occupant control behavior in a heating environment encompasses adjustments in clothing, opening windows, and using heating equipment. The thermal response of occupants in CBF mode, as analyzed in sections 3.3, 3.4, seems to be generally superior to that of occupants in CBA mode. Furthermore, occupants in CBF mode appear to be less dependent on high-temperature heating environments. The economic differences between the two charging modes, as discussed in Section 3.1, could be the reason why occupants’ heat use behavior differs. The heating habits of occupants in this study pertain to their usage of space, duration, intensity, clothing, and window-opening with respect to their heating equipment during the heating season. Section 3.4 employs an expected temperature calculation where CBF mode yields an anticipated temperature of 22.5 °C, whereas CBA mode predicts 23.6 °C. Therefore, an average of 23 °C is utilized as the threshold, where temperatures above 23 °C are classified as the “higher temperature range”, and those below 23 °C are classified as the “lower temperature range” for the purpose of segmentation analysis.

3.4.1 Heating room difference

The variation in heating equipment usage space is mainly determined by the selection of heated rooms. For the choice of heating room, we added corresponding questions in the questionnaire for subjects to choose their own use habits. As presented in Fig. 11, occupants using CBA mode choose to heat all of their rooms, while occupants utilizing CBF mode opt for a more diverse range such as bedrooms, living rooms, studies, kitchens, etc. occupants using CBA mode that have already paid in full are disinclined to adjust their heating equipment, even if the temperature exceeds the desired level, since the cost is fixed. CBF occupants are more probable to switch off the heating in rooms that are not frequently used, resulting in a more moderate compromise between coziness and energy preservation.Fig. 11 Heating room difference between the two charging modes.

Fig. 11

3.4.2 The degree of heating valve opening

The use intensity of heating equipment is mainly composed of “the degree of heating valve opening”. The use of valve adjustment in heating equipment is a crucial aspect of behaviour adjustment in central heating buildings [39]. This survey classifies the opening degree of the valve into five categories: fully open, >1/2, 1/2, <1/2, and closed.

Furthermore, weighted linear regression analysis was conducted on the degree of valve opening for both high and low temperature ranges, with 23 °C serving as the dividing line and taking into account section 3.5. The findings indicate that the slope of the regression equation was statistically significant (P < 0.05) in CBF mode, but not in CBA mode (P > 0.05). The confidence interval of the two curves in the figure is −0.068∼-0.021 respectively, which is within the range of 95 % confidence interval.This suggests that the valve opening degree remains constant in CBA mode occupants despite temperature changes. Additionally, the covariance test confirmed that the slopes of both modes in Fig. 12 (a) and (b) differed significantly (P < 0.05). As depicted in Fig. 12, CBA occupants exhibit a higher valve opening degree in high and low temperature ranges. On the other hand, CBF occupants experience an increase in the valve opening degree in low temperature ranges with the increase of operative temperature. In the higher temperature range, the valve opening degree of CBF occupants is gradually reduced due to the indoor temperature rise. The preceding analysis demonstrates that individuals regulate the valve more actively with temperature changes in CBF mode. Conversely, in CBA mode, the psychological impact of a fixed heating cost leads to people being disinclined to adjust the temperature control valve in line with the indoor operative temperature and their personal preferences.Fig. 12 Heating valve opening degree difference between CBA mode and CBF mode at the same operative temperature.

Fig. 12

3.4.3 Daily heating equipment usage time

The duration of heating equipment usage indicates occupants' satisfaction with the current indoor thermal environment. Longer usage times suggest that residents desire a warmer environment and are unable to tolerate colder conditions. Fig. 13 illustrates the outcome of a linear regression analysis conducted on the duration of usage and occupant heating equipment's operative temperature. The regression equation in the CBA model was found to lack significant significance (P > 0.05). A covariance test, however, demonstrated significant differences (P < 0.01) in the slopes of the two modes depicted in Fig. 13, the confidence interval of the two curves in the figure is −0.053∼-0.022 respectively, which is within the range of 95 % confidence interval. Notably, the heating equipment in the CBA mode is used continuously, i.e. throughout the day, irrespective of temperature fluctuations. The temperature control valve remains unchanged under the CBA mode when the indoor temperature reaches a comfortable level. However, the CBF mode involves a dynamic adjustment process. In the lower temperature range, occupants under the CBF mode increase the use time of heating equipment due to excessively low indoor temperature. This continues until the temperature reaches the higher range, at which point it stabilizes.Fig. 13 Heating time difference between CBA mode and CBF mode at the same operative temperature.(R2 = 0.658).

Fig. 13

3.4.4 Clothing regulation behavior

The preceding analysis has revealed notable distinctions in the duration, extent, and intensity of heating valve utilization between the two modes. As the principal parameter governing thermal acclimatization [40], is there also a disparity between the two modes? A weighted linear regression was conducted to examine the relationship between indoor operative temperature and occupant clothing thermal resistance under two different modes. The findings indicated that there was a significant linear relationship (P < 0.05) between the regression equations of the two charging modes, as evidenced by Fig. 14In both modes, there was a decrease in the thermal resistance of occupant clothing with an increase in indoor operative temperature. The covariance test revealed no significant difference in slope between both groups (P > 0.05). Nonetheless, a significant difference in intercept was found (P < 0.05). At the same operative temperature, the thermal resistance of clothing in CBF occupants was considerably higher than that of CBA occupants.Fig. 14 The relationship between clothing insulation and operative temperature.

Fig. 14

3.4.5 Window behavior

When people adjust clothes to improve heat perception, window opening behavior is also one of the effective methods. Using the same method as section 3.5.1, a questionnaire survey was conducted on the “window opening situation” of occupants under the two charging modes. Results As shown in Fig. 15(a) and (b), there is no significant difference in window opening between CBF and CBA occupants at a lower temperature range (P > 0.05), while there is a significant difference in window opening when the temperature continues to rise to 23 °C above the average expected temperature (P < 0.05). The window opening ratio of CBA occupants is much higher than that of CBF occupants, which indicates that CBA occupants will choose to open Windows more for heat dissipation when facing higher indoor heating temperature in winter, which is unfavorable to energy saving.Fig. 15 Comparison of window opening of occupants with two charging modes.

(ns represents P > 0.05, * represents P < 0.05, ** represents P < 0.001).

Fig. 15

Therefore, the differences between the two modes mentioned above can be attributed to the psychological differences among residents caused by the differences in the amount of heating fees and the collection methods, which are manifested in the differences in the ways and degrees of using heat to control behaviors.

4 Discussion

4.1 Comparison of thermal comfort between the two modes

4.1.1 Significant differences in control behavior and patterns

In terms of the space, intensity and duration of heating equipment, CBA occupants usually adopt the full-time and full-space mode, and generally do not regulate the heating valve. Although CBA occupants can still adjust the heating equipment, they are reluctant to adjust it due to high costs, one-time collection methods and other reasons. On the contrary, CBF is more flexible and diverse in the use of heating space, and more active in regulating the opening degree of the valve and the duration of use with the change of temperature. Secondly, in the thermal adaptation behavior, there are also obvious differences in clothing adjustment behavior and window opening behavior between the two charging modes.

4.1.2 Thermal comfort difference

Although there are significant differences in control behaviors between the two modes, there are no significant differences in indoor and outdoor thermal environments, which is because people have different regulation modes. For example, in the CBA mode, when there is a risk of overheating in the indoor environment, they will deal with thermal discomfort by reducing clothing or opening windows. However, in the CBF mode, people will actively limit the use of heating valves, adjust the opening degree and usage time of valves, even increase clothing and reduce window opening to regulate the indoor thermal environment. Therefore, in the case of no significant difference in indoor thermal environment, the CBF mode has lower discomfort votes, more users expect the same environmental parameters, and higher acceptability and satisfaction with air quality. As can be seen from the analysis in Section 3.6, the thermal sensation in the CBF mode feels warmer in a low-temperature environment, and the neutral temperature and expected temperature of residents are lower, and they are more able to accept lower temperatures.

Compared with CBA mode, CBF mode has a more obvious advantage in terms of comfort and carbon reduction.

4.2 The influence of control behaviour on subjective response and air quality perception in heating buildings

4.2.1 The influence of control behaviour on subjective thermal response in heating buildings

The influence of economic factors on people's psychology leads to the difference of control behavior, which has been continuously confirmed in recent years. Zhou et al. [41] found that in summer, among the residents, the thermal sensation of those who have the ability to control air conditioning is higher than that of those who do not. Meanwhile, it has been confirmed that thermal control behavior has an impact on thermal response. Luo et al. [34] conducted a survey on residents with District heating supply (uncontrollable) and Individual occupant gas boiler heating in winter, and found that the neutral temperature of the subjects with personal control was 2.6 °C lower than that of the subjects with uncontrolled District heating, and they had lower expectations to change the current thermal environment. When the indoor temperature deviated from 18 °C, compared with the subjects without perception control, the thermal sensation of the subjects with perception control was more neutral. The lower the indoor temperature in winter, the higher the improvement effect of perception control on thermal sensation. Langevin et al. [42] analyzed the ASHRAE RP-884 database and confirmed that the thermal sensation of the subjects in the high control group was more neutral.

The above research shows that the existing research on the effect of perception control on thermal response is mainly obtained through the comparative analysis between central heating and other controllable heating equipment, and most of them are limited to qualitative research. In central heating buildings, there is still no quantitative research on the range and extent of the impact of the difference in control behavior caused by the different charging modes on thermal response. To solve the above problems, 2 °C is taken as a temperature interval, which is divided into five temperature intervals: <18 °C, 18–20 °C, 20–22 °C, 22–24 °C, 24–26 °C, and the thermal comfort, thermal feeling and thermal expectation of residents under the same operative temperature are compared and analyzed.

As shown in Fig. 16 (a), there were no significant differences in thermal sensation between the two types of occupants at the three temperature segments of 18–24 °C (P > 0.05), while there were significant differences in the two temperature segments of <18 °C and 24–26 °C (P < 0.05). According to Section 3.3, the two critical temperatures of 18 °C and 24 °C are similar to the thermal neutral temperature of 18.8 °C and the expected temperature of 23.9 °C. Before 18 °C, the thermal sensation of CBF occupants was significantly higher than that of area occupants, and at 24–26 °C, the thermal sensation of CBF occupants was also closer to neutral (P < 0.01), which reflected that the flow charging model endowed people with stronger temperature tolerance under hot or cold environments. The above analysis shows that the influence of thermal control behavior on thermal sensation in winter is mainly in the cold or warm environment, while the sensory control does not affect thermal sensation in the relatively comfortable temperature segment in the middle.Fig. 16 The difference in subjective response between CBA mode and CBF mode occupants.

(ns represents P > 0.05, * represents P < 0.05, ** represents P < 0.001).

Fig. 16

The influence of perceived control on thermal comfort is more significant in winter, as shown in Fig. 16 (b). It can be seen that in all operative temperature ranges, the thermal comfort of CBF occupants is higher than that of area occupants (P < 0.05), which is the same as Yan et al.'s [26] conclusion that perception control mainly affects thermal comfort in split air-conditioned buildings in summer. By comparing the average voting values of the two modes of occupant thermal comfort in each temperature range (0.56, 0.17, 0.15, 0.19 and 0.27 respectively in the temperature range from low to high), it was found that the biggest difference was found at < 18 °C and 24–26 °C, indicating that the higher degree of thermal control behavior had a higher degree of improvement on thermal comfort in both hot and cold environments. And the impact is greatest below 18 °C, which allows people to improve thermal comfort at lower heating temperatures.

It can be seen from the analysis in Section 3.6 that CBF mode improves people's tolerance to low temperature environment and reduces their expectations to high temperature heating environment. Fig. 17 further analyzes the differences in winter control behaviors on the percentage of “expected heating” in different heating temperature ranges. The results show that there are significant differences in the four temperature ranges of <18 °C, 18–20 °C, 20–22 °C and 22–24 °C (P < 0.05), but there is no significant difference in the temperature range of 24–26 °C (P > 0.05), indicating that the influence of perception control on people's “expected heating” is smaller in the higher temperature ranges and larger in the lower temperature ranges. The differences in the percentage of “expected heating” options of residents in different temperature ranges under the two modes are compared and analyzed, and this percentage difference is defined as the effect of “expected improvement” (Fig. 18). It is found that with the increase of indoor operative temperature, the effect of expected improvement gradually decreases (25.1 %, 18.6 %, 14.1 %, 6.4 %, 2 %), indicating that in the lower temperature range of <18 °C, perception control is better at reducing people's expectations to high temperature environment, which is conducive to achieving comfort at lower heating temperatures.Fig. 17 Percentage of wanted warmer between CBA mode vs. CBF mode at the different operative temperature. (ns represents P > 0.05, * represents P < 0.05, ** represents P < 0.001).

Fig. 17

Fig. 18 Thermal expectation improvement trend with operative temperature.

Fig. 18

In summary, control behavior plays an important role in the thermal comfort, thermal sensation and thermal expectation of human body in the winter heating environment, with different degrees of influence. Thermal comfort is the most improved by the difference of control behavior, and is positively affected in each temperature range; thermal sensation, an indicator of people's direct feelings to the environmental cold and hot stimulation.In the cold (below 18 °C) and hot (24–26 °C) environment, the flow occupants are more likely to achieve thermal neutrality. The influence of thermal control behavior on thermal expectation is linear with the change of operative temperature, and the improvement of people's high temperature expectation is most significant in the lower temperature range. In general, using heat to control behavior can improve people's feelings in uncomfortable environments such as hot or cold, and the improvement effect is better in the lower temperature range.

4.2.2 The difference of air quality perception at different operative temperature ranges

CO2 is usually considered as one of the indicators to evaluate the degree of health in the indoor environment of buildings [43,44].

This study takes CO2 concentration as the object and compares the differences in indoor CO2 and PM2.5 concentrations under two charging modes, as shown in Fig. 19, Fig. 20, and finds that there is no significant difference in CO2 and PM2.5 concentrations under the two modes (P > 0.05).Fig. 19 Comparison of CO2 concentration between two charging modes. (ns represents P > 0.05, * represents P < 0.05, ** represents P < 0.001).

Fig. 19

Fig. 20 Comparison of PM2.5 concentration between two charging modes. (ns represents P > 0.05, * represents P < 0.05, ** represents P < 0.001).

Fig. 20

Torriani et al. [45] found in their study that the air quality satisfaction in classroom buildings was inversely proportional to the indoor operative temperature, but comparatively speaking, the air quality satisfaction of the subjects with perceived control was always better. This study compared and analyzed the air quality satisfaction in different temperature ranges. As shown in Fig. 21, the air quality satisfaction of residents in CBF mode was significantly higher than that in CBA mode in the relatively neutral temperature range of 18–24 °C (P < 0.005), but there was no significant difference between the two when the relatively higher temperature range of 24–26 °C exceeded the expected temperature. The influence of the difference in the degree of behavioral control with heat on the perception of air quality existed in the relatively neutral operative temperature range, but disappeared when the expected temperature reached 23.6 °C.Fig. 21 The difference in subjective response between CBA mode and CBF mode occupants. (ns represents P > 0.05, * represents P < 0.05, ** represents P < 0.001).

Fig. 21

The reason may be that when the environment people are in reaches the expected temperature value, the more expected temperature will weaken people's initiative to feel the air quality, and the psychological effect of behavioral control on the perception of air quality is no longer significant (P > 0.05).

5 Conclusion

In order to study the effects of different heating charging methods on indoor thermal environment and human thermal response, and to determine the thermal comfort evaluation of central heating buildings, the indoor thermal environment and thermal response of residential buildings with different operation modes in Jiaozuo, China in winter were studied. The conclusions of this study are as follows:1) The two heating charging modes have a significant impact on the time, space and intensity of people's use of heating equipment. For CBA occupants, due to the heating charging according to the area, people seldom restrict the use of heating equipment, heating valves are used all the time and all the space, and the heating valves are fully open, which will not change with the change of temperature. For CBF occupants, the use of heating equipment is more restricted. When the indoor temperature is lower than 23 °C, the degree of valve opening and the length of use will increase with the increase of indoor temperature; when the indoor temperature is higher than 23 °C, the degree of valve opening will decrease with the increase of indoor temperature, but the length of use changes little, and people mainly regulate the degree of valve opening.

2) Different heating charging modes have different effects on residents' clothing and window opening behavior. The thermal resistance of residents' clothing in CBF mode is significantly higher than that in CBA mode. At the same temperature, the thermal resistance of residents' clothing in CBF mode is significantly higher than that in CBA mode, and people are more active in adjusting their clothing. Difference analysis of window opening behavior.

3) Although there were no significant differences in indoor and outdoor environmental parameters between the two modes, compared with the CBA mode, the CBF residents had more warm thermal feelings, thermal comfort, thermal satisfaction and thermal acceptability were significantly higher than those of the CBA residents, and thermal expectations were more likely to remain unchanged.

4) Compared with CBA occupants, CBF occupants have lower thermal neutral temperature, lower expected temperature and lower acceptable temperature. CBF mode reduces people's expectation of high temperature and expands the acceptable range of low temperature. Compared with CBA mode, CBF mode occupants have more obvious advantages between comfort and carbon reduction.

5) The influence interval and degree of perception control on thermal response are not the same. Perception control has no effect on thermal sensation in the temperature range of 18–24 °C, but can significantly improve people's thermal sensation in the temperature range below 18 °C and above 24 °C. Thermal comfort is most affected by perception control, and in all operative temperature ranges, the thermal comfort of flow residents is higher than that of area residents. The influence of perception control on thermal expectation is linear with the change of operative temperature, and the improvement of people's high temperature expectation is most significant in the lower temperature range.

6) When the indoor temperature is below 24 °C, the air quality satisfaction of residents in CBF mode is significantly higher than that in CBA mode, which indicates that the CBF mode can significantly improve people's satisfaction with indoor air quality.

6 Limitations and expectation

1) In this study, the control behavior of residents consists of the regulation mode of heating equipment, clothing regulation behavior and window opening behavior. However, in summer, the control behavior will involve more, such as fan use behavior, drinking cold drinks, etc. The intervention of more control behaviors may also have different degrees of impact on residents' thermal response.

2) For the testing of air quality satisfaction and the detection of indoor pollutants (CO2, PM2.5), due to the quantity of experimental instruments and the difficulty of field survey, the data collected is relatively small (<200). The sample size of the study can be increased in the future to increase the universality of the results.

3) In the following research, the influence of control behavior on thermal response can be discussed according to different seasons. Meanwhile, the specific influencing factors of perceived control, such as age and gender, can be discussed in depth.

Ethics and consent

Due to the public availability of the dataset from this research, the relevant ethical consent issues are stated as follows.1) Verbal informed consent: This study has obtained verbal informed consent from the participants to publish this paper along with any accompanying data and images. Since the survey does not involve significant economic issues or major secrets beyond heating costs, and due to the large number of participants, a written informed consent form is neither required nor feasible.

2) Ethics approval: This study is a survey conducted through visits and does not involve any direct medical, physiological, or psychological interventions to the participants. According to local laws, formal ethics approval is not required for this study.

3) Consent for minors: If the participants are minors, we adhere to local laws regarding the age and circumstances under which they can consent on their own. If they are not of legal consenting age, consent has been obtained from both the minor and their authorized proxy (i.e., parents or legal guardians).

4) Anonymization of data: All content, relevant data, and images presented in the published paper and publicly available datasets have been anonymized to the greatest extent possible.

5) Information provided to participants: Participants/legal guardian(s) have been fully informed about the purpose of this case report, the potential risks and benefits of publication, and the consequences of disclosing their personal information.

6) Voluntary participation: Participants or legal guardians have been informed that their consent and participation in the publication of this case report are entirely voluntary. They have been made aware that they have the right to withdraw consent at any time.

7) Compliance with local laws: This study complies with local laws regarding consent and privacy.

Data availability statement

Data associated with our study has been deposited into a publicly available repository. The dataset from this study have been stored in the Open Science Framework (OSF, https://osf.io) repository, with the access number https://doi.org/10.17605/OSF.IO/E9WSD.

CRediT authorship contribution statement

Haiyan Yan: Funding acquisition, Formal analysis, Data curation, Conceptualization. Linxiao Xie: Writing – original draft, Software, Resources, Project administration, Methodology, Investigation. David Chow: Supervision, Conceptualization. Xu Chen: Visualization. Meng Li: Methodology, Investigation.

Declaration of competing interest

We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work. All authors have read and approved this version of the article, and due care has been taken to ensure the integrity of the work. No part of this paper has published or submitted elsewhere. No conflict of interest exits in the submission of this manuscript.

Acknowledgments

This study was supported by 10.13039/501100001809 National Natural Science Foundation of China (Grant No. 52378095 ); Key Research Projects of Higher Education Institutions in Henan Province (Grant No. 24A410001 ).
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References

1 Zhang Q. Kong J. Jiang M. Building energy consumption prediction based on temporal-aware attention and energy consumption states J. Elec. Eng. Technol. 18 1 2023 61 75 https://10.1007/s42835-022-01159-3
2 Mi Z. Zheng J. Meng J. China's energy consumption in the new normal Earth's Future 10 2018 1029 10.1029/2018EF000840
3 Zhu W. Huang B. Zhao J. Impacts on the embodied carbon emissions in China's building sector and its related energy-intensive industries from energy-saving technologies perspective: a dynamic CGE analysis [J] Energy Build. 287 2023 112926 10.1016/j.enbuild.2023.112926
4 Zhang W. Cui Y. Wang J. How does urbanization affect CO2 emissions of central heating systems in China? An assessment of natural gas transition policy based on nighttime light data J. Clean. Prod. 276 2020 123188 10.1016/j.jclepro.2020.123188
5 Du C. Li B. Yu W. Energy flexibility for heating and cooling based on seasonal occupant thermal adaptation in mixed-mode residential buildings Energy 189 2019 10.1016/j.energy.2019.116339
6 Luo W. Kramer R. Kompier M. Effects of correlated color temperature of light on thermal comfort, thermophysiology and cognitive performance [J] Build. Environ. 231 2023 109944
7 Venzhik Y.V. Deryabin A.N. Zhukova K.V. Features of formation of wheat's increased cold resistance under the influence of gold nanoparticles Russ. J. Plant Physiol. 71 1 2024
8 Wang F. Ke Y. Zheng Q. Occupant behavioral adjustments and thermal comfort with torso and/or foot warming in two cold indoor environments Build. Environ. 2024 257
9 Mosteiro-Romero Martín Quintana M. Stouffs R. A data-driven agent-based model of occupants' thermal comfort behaviors for the planning of district-scale flexible work arrangements Build. Environ. 2024 257
10 Nicol J.F. Thermal comfort as part of a self-regulating system Build. Res. Prac. 6 3 1973 174 179 10.1080/09613217308550237
11 Nikolopouluo M. Steemers K. Thermal comfort and psychological adaptation as a guide for designing urban spaces [J] Energy Build. 35 1 2003 95 101 10.1016/S0378-7788(02)00084-1
12 Rijal H.B. Humphreys M.A. Nicol J.F. Adaptive model and the adaptive mechanisms for thermal comfort in Japanese dwellings [J] Energy Build. 202 11 2019 109371.1 109371.14 10.1016/j.enbuild.2019.109371
13 Bzy A. Dac X. Hxa B. Investigation of thermal comfort of room air conditioner during heating season [J] Build. Environ. 207 2022 108544 10.1016/j.buildenv.2021.108544
14 Gucyeter B. Evaluating diverse patterns of occupant behavior regarding control-based activities in energy performance simulation Front. Architect. Res. 7 2 2018 10.1016/j.foar.2018.03.002
15 Deuble M.P. de Dear R.J. Green occupants for green buildings: the missing link? [J] Build. Environ. 56 2012 21 27 10.1016/j.buildenv.2012.02.029
16 Yun G.Y. Influences of perceived control on thermal comfort and energy use in buildings [J] Energy Build. 158 2018 822 830 10.1016/j.enbuild.2017.10.044
17 Zhou X. Ouyang Q. Zhu Y. Experimental study of the influence of anticipated control on human thermal sensation and thermal comfort Indoor Air 24 2 2014 171 177 10.1111/ina.12067 23980928
18 Lamberti G. Boghetti R. Kämpf J.H. Development and comparison of adaptive data-driven models for thermal comfort assessment and control[J] Total Environ. Res. Themes 8 2023 100083
19 Xu C. Li S. Influence of perceived control on thermal comfort in winter, A case study in hot summer and cold winter zone in China [J] J. Build. Eng. 40 2021 102389 10.1016/j.buildenv.2023.110872
20 Nakaya T. Matsubara N. Kurazumi Y. Use of Occupant Behaviour to Control the Indoor Cli Mate in Japanese Residences [J] 2008 Researchgate 27 29 http://nceub.org.uk
21 Nicol J.F. Adaptive thermal comfort and sustainable thermal standards for buildings [J] Energy Build. 34 6 2002 563 572 10.1016/S0378-7788(02)00006-3
22 Zeiler W. Boxem G. Effects of thermal activated building systems in schools on thermal comfort in winter [J] Build. Environ. 44 11 2009 2308 2317 10.1016/j.buildenv.2009.05.005
23 Chung J.D. Hong H. Yoo H. Analysis on the impact of mean radiant temperature for the thermal comfort of underfloor air distribution systems Energy Build. 42 12 2010 2353 2359 10.1016/j.enbuild.2010.07.030
24 Hao X. Zhang G. Chen Y. A combined system of chilled ceiling, displacement ventilation and desiccant dehumidification [J] Build. Environ. 42 9 2007 3298 3308 10.1016/j.buildenv.2006.08.020
25 Catalina T. Virgone J. Kuznik F. Evaluation of thermal comfort using combined CFD and experimentation study in a test room equipped with a cooling ceiling [J] Build. Environ. 44 8 2009 1740 1750 10.1016/j.buildenv.2008.11.015
26 Yan H. Sun Z. Shi F. Thermal response and thermal comfort evaluation of the split air conditioned residential buildings J. Build. Environ. 221 2022 109326 10.1016/j.buildenv.2022.109326
27 Wu Y. Liu H. Li B. Thermal adaptation of the elderly during summer in a hot humid area: psychological, behavioral, and physiological responses Energy Build. 203 45 2019 109450 10.1016/j.enbuild.2019.109450
28 Yan H. Yang L. Zheng W. Influence of outdoor temperature on the indoor environment and thermal adaptation in Chinese residential buildings during the heating season Energy Build. 116 2016 133 140 10.1016/j.enbuild.2015.12.053
29 Zhao Q. Xu Y. Li Z. Influence of residents' behaviors on space heating load in villages Build. Sci. 35 4 2019 96 100 (in Chinese)
30 ASHRAE ANSI/ASHRAE Standard 55-2020: Thermal Environmental Conditions for Human Occupancy [S] 2020 American Society of Heating, Refrigerating and Air Conditioning Engineers Atlanta, Georgia
31 Gagge A.P. Stolwijk J.A.J. Hardy J.D. Comfort and thermal sensations and associated physiological responses at various ambient temperatures Environ. Res. 1 1 1967 1 20 5614624
32 Luo M. Cao B. Damiens J. Evaluating thermal comfort in mixed-mode buildings: a field study in a subtropical climate Build. Environ. 88 2015 46 54 10.1016/j.buildenv.2014.06.019
33 Jia X. Cao B. Zhu Y. Thermal comfort in mixed-mode buildings: a field study in Tianjin, China [J] Build. Environ. 185 2020 107244 10.1016/j.buildenv.2020.107244
34 Cena K. de Dear R. Thermal comfort and behavioural strategies in office buildings located in a hot-arid climate J. Therm. Biol. 26 4–5 2001 409 414 10.1016/S0306-4565(01)00052-3
35 Spagnolo J. de Dear R. A field study of thermal comfort in outdoor and semi-outdoor environments in subtropical Sydney Australia Build. Environ. 38 5 2003 721 738 10.1016/S0360-1323(02)00209-3
36 International Organization for Standardization 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: ISO International Standard 7726-2001 [S] 2001 International Organization for Standardization Genova
37 Luo M. Cao B. Zhou X. Can personal control influence human thermal comfort? A field study in residential buildings in China in winter Energy Build. 72 2014 411 418 10.1016/j.enbuild.2013.12.057
38 Cao B. Zhu Y. Min L. Individual and district heating: a comparison of residential heating modes with an analysis of adaptive thermal comfort Energy Build. 78 8 2014 17 24 10.1016/j.enbuild.2014.03.063
39 Chen C. The Study on Heat Usingmode in Bulidings Adopting Heat Metering Systems [D] 2013 Hebei: University of Technology Dissertation (in Chinese)
40 Tabaie Z. Omidvar A. Kim J. Non-uniform distribution of clothing insulation as a behavioral adaptation strategy and its effect on predicted thermal sensation in hot and humid environments Energy Build. 271 2022 112310 10.1016/j.enbuild.2022.112310
41 Zhou X. Zhu Y. Ouyang Q. Experimental study on the influence of environmental control ability on human thermal sensation Build. Sci. 26 10 2010 177 180 (in Chinese)
42 Langevin J. Wen J. Gurian P.L. Relating occupant perceived control and thermal comfort: statistical analysis on the ASHRAE RP-884 database Heating, Ventilation, Air Conditioning, and Refrigeration and Related Technologies Research 18 1–2 2012 179 194
43 Fantozzi F. Lamberti G. Leccese F. Monitoring CO2 concentration to control the infection probability due to airborne transmission in naturally ventilated university classrooms Architect. Sci. Rev. 65 4 2022 306 318
44 Lu Y. Huang J. Wagner D.N. The influence of displacement ventilation on indoor carbon dioxide exposure and ventilation efficiency in a living laboratory open-plan office[J] Build. Environ. 256 2024 111468
45 Torriani G. Lamberti G. Fantozzi F. Exploring the impact of perceived control on thermal comfort and indoor air quality perception in schools J. Build. Eng. 63 2023 105419
