A Systematic Review on the Studies of Thermal Comfort in Urban Residential Buildings in China
Abstract
:1. Introduction
2. Methods
3. Research Methods for Thermal Comfort
3.1. Field Studies
3.1.1. Procedure of Field Studies
3.1.2. Survey
3.1.3. On-Site Measurement
3.2. Laboratory Studies
3.3. Comparison between Field Studies and Laboratory Studies
4. Thermal Comfort Evaluation Indices
4.1. PMV/PPD
4.2. TSV
4.3. MTS
4.4. TCV
4.5. Operative Temperature
4.6. Neutral Temperature
4.7. Thermal Preference Vote
4.8. Thermal Acceptability
4.9. Preferred Temperature
4.10. Thermal Adaptation
4.11. Other
5. Discussion and Analysis
5.1. Thermal Comfort Field Investigation
5.2. Acceptable Temperature Range
5.3. Yearly Variation in Neutral Temperature
5.4. Variation in Neutral Temperature with Climate Region
5.5. Variation in Neutral Temperature with Clothing Thermal Resistance
5.6. Variation in Neutral Temperature with Indoor Air Temperature
5.7. Variation in Clothing Thermal Resistance with Indoor and Outdoor Air Temperatures
5.8. Adaptive Model
5.9. Adaptive Behavior
5.10. Implications and Limitations of the Findings
5.10.1. Implications of the Findings
5.10.2. Limitations of the Findings
6. Conclusions
- (1)
- More investigations were conducted through field studies than laboratory studies. The field studies are strong in collecting actual and large occupant samples but weak in controlling the environmental parameters, which can be compensated by conducting laboratory studies.
- (2)
- The thermal sensation vote, operative temperature, thermal acceptance, mean thermal sensation, and predicted mean vote are the most commonly used thermal indices. The evaluation of the thermal comfort sensation might vary with different indices, and therefore, it is recommended to use multiple indices for evaluation.
- (3)
- People have higher and higher requirements for the indoor thermal environment. In severe cold and cold regions, the thermal neutral temperature in the winter tends to increase with time, and people have higher requirements for the indoor thermal environment in the winter.
- (4)
- The thermoneutral temperature varies in different seasons. There is little difference in the thermoneutral temperatures of different climate zones in the summer, but they vary greatly in the winter. In the summer, the thermoneutral temperature is higher in warmer climate zones and it is the contrary in the winter. Whether it is winter or summer, the thermoneutral temperature tends to increase with the indoor air temperature due to an adaptation to the indoor thermal environment.
- (5)
- The thermoneutral temperature of the human body is affected by the clothing thermal resistance. The correlation between the clothing thermal resistance and the thermoneutral temperature is weak in the summer and strong in the winter.
- (6)
- In the summer, the thermoneutral temperature is higher in warmer climate regions. However, in the winter, the thermoneutral temperature is lower in warmer climate regions.
- (7)
- In the same climate region, the neutral temperature in the summer is greater than the neutral temperature in the winter, especially in hot summer and cold winter regions and hot summer and warm winter regions.
6.1. Future Research Directions
6.2. Challenges
6.3. Broader Implications of the Study
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
Year | City | Climate Region | Season | Note | Average Outdoor Temperature (°C) | Average Indoor Temperature (°C) | Neutral Temperature (°C) | Preferred Temperature (°C) | 80% Acceptability Temperature (°C) | Average Clothing Thermal Resistance | Ref. |
---|---|---|---|---|---|---|---|---|---|---|---|
2018 | Tianjin | Cold | Summer | -- | 21.3 | -- | 25.4 | -- | 21.0–27.3 | -- | [16] |
2009 | Hunan | Hot summer and cold winter | Winter | -- | -- | 12.1 | 14.0 | -- | -- | 2.0 | [58] |
2006 | Harbin | Severe cold | Winter | Men | -- | -- | 20.9 | -- | -- | 1.33 | [41] |
2006 | Harbin | Severe cold | Winter | Women | -- | -- | 21.9 | -- | -- | 1.42 | [41] |
2014 | Beijing | Cold | Winter | Central heating | -- | 21.3 | 20.7 | -- | -- | 0.86 | [105] |
2014 | Beijing | Cold | Winter | Independent heating | -- | 21.2 | 18.1 | -- | -- | 0.87 | [105] |
2014 | Shanghai | Hot summer and cold winter | Winter | -- | -- | 16.5 | 17.2 | -- | -- | 1.45 | [105] |
2022 | Inner Mongolia | Severe cold | Winter | -- | −1.8 | 26.9 | 24.8 | -- | 21.9–27.5 | 0.43 | [15] |
2018 | -- | Severe cold | -- | -- | -- | -- | 20.2 | -- | -- | -- | [20] |
2018 | Xi’an | Cold | -- | -- | -- | -- | 19 | -- | -- | -- | [20] |
2018 | -- | Hot summer cold winter | -- | -- | -- | -- | 18.5 | -- | -- | -- | [20] |
2018 | -- | Hot summer and warm winter | -- | -- | -- | -- | 21 | -- | -- | -- | [20] |
2018 | Kunming | Mild | -- | -- | -- | -- | 22 | -- | -- | -- | [20] |
2017 | Harbin | Severe cold | Winter | Early heating | −10 | 24.3 | 21.6 | -- | -- | 0.85 | [14] |
2017 | Harbin | Severe cold | Winter | Mid heating | −10 | 24.3 | 23.5 | -- | -- | 0.78 | [14] |
2017 | Harbin | Severe cold | Winter | Late heating | −10 | 24.3 | 23.1 | -- | -- | 0.69 | [14] |
2017 | -- | Severe cold | Winter | -- | −19.7 | 19.2 | 19.5 | -- | -- | 1.37 | [21] |
2017 | -- | Cold | Winter | -- | −2.3 | 16.3 | 20.8 | -- | -- | 1.41 | [21] |
2017 | -- | Hot summer and cold winter | Winter | -- | 5.9 | 15.7 | 18.2 | -- | -- | 1.26 | [21] |
2017 | -- | Hot summer and warm winter | Winter | -- | 15.3 | 16.5 | 19.7 | -- | -- | 1.22 | [21] |
2017 | -- | Severe cold | Summer | -- | 26.8 | 24.4 | 25.4 | -- | -- | 0.58 | [21] |
2017 | -- | Cold | Summer | -- | 30.7 | 28.6 | 27.2 | -- | -- | 0.34 | [21] |
2017 | -- | Hot summer cold winter | Summer | -- | 32.1 | 29.4 | 27.6 | -- | -- | 0.32 | [21] |
2017 | -- | Hot summer and warm winter | Summer | -- | 31.7 | 29.7 | 27.5 | -- | -- | 0.34 | [21] |
2018 | Nanjing | Hot summer and cold winter | Summer | -- | 27.3 | -- | 28 | -- | 22–30.1 | 0.46 | [17] |
2018 | Nanjing | Hot summer and cold winter | Winter | -- | 2.4 | -- | 15.8 | -- | 10.6–28.5 | 2.07 | [17] |
2010 | Harbin | Severe cold | Summer | -- | 27.0 | 26.9 | 23.7 | -- | 21.5–31.0 | -- | [44] |
2022 | Jiaozuo | Cold | Summer | Air conditioned | 32.6 | 27.7 | 28.7 | -- | 24.3–30.0 | -- | [50] |
2022 | Jiaozuo | Cold | Summer | Naturally ventilated | 30.2 | 29.6 | 26.9 | -- | 16.2–29.9 | -- | [50] |
2022 | Jiaozuo | Cold | Summer | Hybrid mode | 31.5 | 28.6 | 27.7 | -- | 23.7–30.1 | -- | [50] |
2021 | Beijing | Cold | Winter | Transition period | 12.0 | 19.5 | 24.6 | -- | -- | 1.0 | [55] |
2021 | Beijing | Cold | Winter | Heating period | 5.2 | 22.9 | 21.9 | -- | -- | 0.9 | [55] |
2017 | -- | Hot summer and cold winter | Spring | -- | 20.05 | 20.42 | 21.1 | -- | -- | 0.69 | [59] |
2017 | -- | Hot summer and cold winter | Summer | -- | 29.57 | 28.98 | 24.3 | -- | -- | 0.26 | [59] |
2017 | -- | Hot summer and cold winter | Autumn | -- | 20.31 | 21.07 | 23.8 | -- | -- | 0.60 | [59] |
2017 | -- | Hot summer and cold winter | Winter | -- | 9.57 | 11.78 | 21.1 | -- | -- | 1.30 | [59] |
2016 | Harbin | Severe cold | Winter | -- | −8.9 | 23.9 | 22.0 | -- | -- | 0.96 | [72] |
2016 | Beijing | Cold | Winter | -- | 3.6 | 21.3 | 22.0 | -- | -- | 1.1 | [72] |
2016 | Shanghai | Hot summer and cold winter | Winter | -- | 9.9 | 16.4 | 22.7 | -- | -- | 0.99 | [72] |
2011 | Harbin | Severe cold | Winter | Before heating | 15.9 | 20.6 | 25.1 | -- | 17.5–24.0 | 0.77 | [65] |
2011 | Harbin | Severe cold | Winter | During heating | −3.6 | 21.6 | 20.4 | -- | 19.0–26.5 | 0.88 | [65] |
2008 | -- | Hot summer and cold winter | Summer | Naturally ventilated | -- | 33.0 | 28.3 | 27.9 | 25.0–31.6 | 0.28 | [60] |
2008 | -- | Hot summer and cold winter | Summer | Air conditioned | -- | 28.5 | 27.7 | 27.3 | 25.1–30.3 | 0.32 | [60] |
2023 | Guangzhou | Hot summer and warm winter | Summer | -- | -- | -- | 28.4 | -- | 27.1–29.6 | 0.5 | [49] |
2022 | Jiaozuo | Cold | Winter | Radiator heating | 5.4 | 21.1 | 19.8 | -- | 17.0–29.4 | 1.15 | [51] |
2022 | Jiaozuo | Cold | Winter | Floor heating | 5.4 | 21.5 | 19.2 | -- | 15.9–34.5 | 0.89 | [51] |
2019 | North | -- | Winter | -- | −5.5 | 18.6 | 20.2 | -- | -- | 1.35 | [45] |
2019 | South | -- | Winter | -- | 8.1 | 11.9 | 17.1 | -- | -- | 1.70 | [45] |
2020 | Nanjing | Hot summer and cold winter | Summer | -- | -- | -- | 27.6 | -- | -- | -- | [46] |
2020 | Nanjing | Hot summer and cold winter | Winter | -- | -- | -- | 14.4 | -- | -- | -- | [46] |
2007 | -- | -- | Summer | -- | -- | 29.8 | 28.6 | -- | -- | 0.54 | [64] |
2014 | Xi’an | Cold | Transition season | -- | 13.9 | 18.1 | 21.3 | 23.9 | Lower limit: 11.4 | 1.19 | [70] |
2015 | Yinchuan | Cold | Summer | -- | 29.2 | 28.9 | 25.9 | 21.7 | Upper limit: 28.5 | 0.3 | [103] |
2012 | Jiaozuo | Cold | Summer | -- | 29.5 | 30.2 | Measured: 27.7 Predicted: 25.4 | 27.4 | Thermal sensation: 19.8–28.8 Direct inquiry: 20.7–29.2 | 0.258 | [69] |
2021 | Bayannur | Cold | Winter | City | -- | 26.8 | 24.8 | 25.3 | 20.9–28.0 | 0.43 | [61] |
2021 | Bayannur | Cold | Winter | County | -- | 19.4 | 20.4 | 22.1 | 17.7–23.3 | 0.43 | [62] |
2020 | Guilin | Hot summer& cold winter | Summer | -- | 28.3 | 30.8 | Predicted: 25.4 Measured: 24.8 | 25.8 | Predicted: 22.8–28.0 Measured: 21.0–28.5 | -- | [62] |
2021 | Jiaozuo | Cold | Summer | -- | 30.4 | 28.6 | 27.6 | -- | Upper limit: 29.9 | 0.29 | [68] |
2019 | Xi’an | Cold | Winter | -- | -- | -- | 21.0 | -- | -- | 0.5 | [63] |
2021 | Temperate continental | Cold | Summer | -- | -- | -- | 24.0 | -- | -- | -- | [57] |
2021 | Monsoon climate | Cold | Transition season | -- | -- | -- | 20.9 | -- | -- | -- | [57] |
2021 | Temperate continental | Cold | Summer | -- | -- | -- | 25.7 | -- | -- | -- | [57] |
2021 | Monsoon climate | Cold | Transition season | -- | -- | -- | 22.1 | -- | -- | -- | [57] |
2021 | Temperate marine | Cold | Summer | -- | -- | -- | 26.2 | -- | -- | -- | [57] |
2021 | Monsoon climate | Cold | Transition season | -- | -- | -- | 22.3 | -- | -- | -- | [57] |
2022 | Temperate marine | Hot summer & cold winter | Winter | -- | 9.37 | 11.47 | Measured: 13.6 Predicted: 14.2 | -- | -- | 1.65 | [133] |
2022 | Monsoon climate | Hot summer & cold winter | Transition season | -- | -- | -- | 19.9–26.9 | -- | 16.3–30.7 | 0.69 | [66] |
2022 | Temperate continental | Hot summer & cold winter | Transition season | -- | -- | -- | 20.1–29.0 | -- | 16.1–30.7 | 0.96 | [66] |
2022 | Desert climate | Severe cold | Summer | -- | 27.1 | 28.1 | Measured: 24.9 Predicted: 25.2 | 24.6 | 23.8–26.9 | 0.287 | [107] |
2022 | Temperate continental | Cold | Winter | Before heating | 12 | 19.9 | 24.6 | -- | 20.5–29.2 | 1.1 | [73] |
2022 | Desert climate | Cold | Winter | After heating | 5.2 | 22.7 | 21.9 | -- | 20.7–27.3 | 0.9 | [73] |
1999 | Yancheng | Cold | Summer | -- | -- | 28.6 | 26.7 | -- | Upper limit: 30 | 0.31 | [15] |
2006 | Shanghai | Hot summer & cold winter | Summer | -- | -- | -- | 22.5 | -- | 14.7–29.8 | -- | [71] |
2014 | Wuhan | Cold | Winter | District heating | −2.1 | 20.4 | 22.0 | -- | -- | 0.92 | [118] |
2014 | Baotou | Cold | Winter | Independent heating | −2.1 | 18.8 | 18.6 | -- | -- | 0.92 | [118] |
2018 | Beijing | Severe cold | Winter | Early heating | 1.4 | 23.6 | 21.6 | -- | -- | 0.74 | [53] |
2018 | Beijing | Severe cold | Winter | Mid heating | −16 | 24.3 | 23.5 | -- | -- | 0.74 | [53] |
2018 | Beijing | Severe cold | Winter | Late heating | 2.2 | 25.0 | 23.1 | -- | -- | 0.74 | [53] |
2016 | Shanghai | Severe cold | Winter | -- | −11.0 | 20.6 | 21.3 | -- | 17.2–25.4 | 0.89 | [52] |
2016 | Beijing | Cold | Winter | -- | −5.6 | 20.9 | 20.3 | -- | 15.4–24.1 | 0.86 | [52] |
2016 | Beijing | Cold | Winter | -- | 5.8 | 17.2 | 20.4 | -- | 9.8–26.2 | 1.18 | [52] |
2021 | Harbin | Hot summer & cold winter | Winter | -- | 2.4 | -- | 16.9 | 15.8 | -- | -- | [56] |
2021 | Harbin | Hot summer & cold winter | Winter | -- | 2.5 | -- | 18.6 | 19.7 | -- | -- | [56] |
2013 | Harbin | Cold | Winter | -- | -- | -- | -- | 18.9 | 13.6–32.4 | 1.44 | [67] |
2013 | Baotou | Cold | Summer | -- | -- | -- | -- | 23.3 | 13.6–32.4 | 0.46 | [67] |
2019 | Yinchuan | Severe cold | Winter | -- | -- | 24.0 | 23.0 | -- | 18.3–27.3 | 0.79 | [54] |
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Item | Subjective Evaluation | Ref. |
---|---|---|
Thermal sensation | −3, cold; −2, cool; −1, slightly cool; 0, neutral; 1, slightly warm; 2, warm; 3, hot | [54,57,58,59,61,62,63,64,65,66,67,68] |
Humidity sensation | −3, very humid; −2, humid; −1, slightly humid; 0, neutral; 1, slightly dry; 2, dry; 3, very dry | [50,51] |
Humidity sensation | −3, much too dry; −2, too dry; −1, slightly dry; 0, just right; +1, slightly humid; +2, too humid; +3, much too humid | [44,47,65] |
Humidity sensation | 3, too dry; −2, dry; −1, slightly dry; 0, just right; +1, slightly humid; +2, humid; +3, too humid | [60,67] |
Wet sensation | −3, very wet; −2, wet; −1, slightly wet; 0, neutral; 1, slightly dry; 2, dry; 3, very dry | [15] |
Air movement sensation or stuffy sensation | −3, very stuffy; −2, stuffy; −1, slightly stuffy; 0, neutral; 1, slightly windy; 2, windy; 3, very windy | [50,68] |
Air movement sensation | −3, much too still; −2, too still; −1, slightly still; 0, comfortable and no breeze; +1, comfortable and slightly breezy; +2, too breezy; +3, much too breezy | [44,65] |
Air movement sensation | −3, very breezy; −2, breezy; −1, slightly breezy; 0, neutral; 1, slightly stuffy; 2, stuffy; 3, very stuffy | [67] |
Thermal comfort | −3, very uncomfortable; −2, uncomfortable; −1, slightly uncomfortable; 1, slightly comfortable; 2, comfortable; 3, very comfortable | [50,51] |
Thermal comfort | 0, comfort; 1, slightly discomfort; 2, discomfort; 3, high discomfort | [15] |
Thermal comfort | 0, comfortable; 1, slightly uncomfortable; 2, uncomfortable; +3, unbearable | [14,54] |
Thermal comfort | −3, unbearable; −2, very uncomfortable; −1, uncomfortable; 0, slightly uncomfortable; 1, comfortable | [47] |
Thermal acceptability | −2, totally unacceptable; 1, barely unacceptable; 1, barely acceptable; 2, totally acceptable | [47,50,51,68] |
Thermal acceptability | −1, unacceptable; 1, acceptable | [15,54,61] |
Thermal preference | −1, cooler; 0, no change; 1, warmer | [15,47,50,51,54] |
Thermal preference | 1, want warmer; 2, no change; 3, want cooler | [58] |
Humidity preference | −1, lower; 0, no change; +1, higher | [44,65] |
Humidity preference | −1, drier; 0, no change; 1, wetter | [47] |
Wet preference | −1, wetter; 0, no change; 1, drier | [15] |
Air movement preference | −1, lower; 0, no change; +1, higher | [40,44,58,65] |
Adaptive strategies | Opening windows/doors, turning on fans, turning on A/C, none | [16] |
Item | Ref. |
---|---|
Air temperature | [45,46,47,48,49,52,53] |
Relative humidity | [57,58,59,61,62,63,64,65,66] |
Air velocity | [14,15,17,20,21,50,51] |
Mean radiant temperature | [58,60,64,69] |
Black bulb temperature | [17,46,53,56] |
PMV–PPD index (thermal comfort meter) | [41,70] |
Globe temperature | [14,15,21,47,50,51,71,72] |
Carbon monoxide concentration | [55] |
Envelope surface temperature | [14,49,57,66] |
Building type | [20,21,55,58,59] |
Room orientation | [20,21,41,57,73] |
Basic building information | [20,21,49,55,57,73] |
Year | Content | Conclusion | Ref. |
---|---|---|---|
2013 | Differences in thermal adaptation to cold indoor environments between people in areas with/without heating systems | People will change their psychological expectations of the thermal environment according to the indoor environment | [75] |
2014 | Effects of air temperature on sleep quality and thermal comfort of sleepers | Human sleep quality is very sensitive to changes in air temperature, and current standards cannot ensure a comfortable sleep environment | [76] |
2015 | Investigating how controlled or uncontrolled thermal environments affect human thermal responses in the summer | About two-thirds of the subjects preferred manual controls rather than automatic controls | [77] |
2016 | Effect of personal control over air-conditioning system on occupant thermal comfort | The subjects’ perceived control ability to the thermal environment improves their thermal comfort; it is recommended to provide occupants with openable windows, fans, terminal controllable adjustment systems, etc., to control their thermal environment | [78] |
2018 | Analysis of the effects of long-term and short-term heat adaptation on human thermal regulation and perception | The high-temperature and high-humidity environment has adverse effects on the human body’s thermal perception, and the average skin temperature of the subjects has physiological adaptability to repeated thermal stimulations | [79] |
2019 | Comparative study on the thermal comfort of central air-conditioned buildings and buildings served by split units in South China; investigate their differences in occupants’ physiological and psychological responses under the same environmental condition; identify the thermal neutrality and acceptable temperature of air-conditioned buildings | There are no significant differences in the thermal sensations and neutral temperatures of the two buildings | [80] |
2020 | Influence of clothing thermal resistance on human thermal comfort | In cold environments, people are used to wearing more heavy clothing on the upper body than the lower body, and it is recommended to distribute the thermal resistance of clothing more evenly to the lower body to keep warm | [81] |
2020 | Comparing the thermal comfort of the thermal environment served by air conditioners and floor heating terminals under steady-state condition | There was no significant difference in thermal comfort between the thermal environment served by air conditioners and floor heating terminals under steady condition | [82] |
2021 | How the local thermal sensation affects the overall thermal sensation after turning on the air conditioning system | After turning on the air conditioner, the feet feel the coldest and have the greatest impact on the overall thermal sensation | [83] |
2021 | Effects of perceived control on indoor thermal comfort in the winter | Perceived control can improve residents’ thermal comfort in the winter | [56] |
2022 | Effects of different air-conditioning terminals on thermal comfort in a non-uniform environment | The radiant ceiling improves the thermal comfort of the forearm and the back of the hand obviously, and the radiant floor improves the thermal comfort of the calf and foot | [84] |
2022 | Indoor thermal comfort for different heating terminals (fan coil units and room air conditioners) under different operating strategies (continuous and intermittent operation) | Except for long-term shutdown, there were no significant differences between different control strategies, including continuous operation and intermittent operation | [85] |
2023 | Thermal adaptation in naturally ventilated and air-conditioned environments in different seasons | Subjects showed a lower level of heat adaptation under the air-conditioned environment in the winter | [86] |
2023 | Differences in thermal comfort between the young and the elderly | The elderly have a time lag and smaller changes in thermal responses than the young when air temperature changes | [87] |
Publication | Scale | Environmental Control | Sample Representation | Number of Subjects | Sample Type | |
---|---|---|---|---|---|---|
Laboratory studies | Less | Small | Strict | Poor | Small | Few |
Field studies | More | Large | Not strict | Excellent | Large | Multiple |
Method | Information | Ref. |
---|---|---|
Direct inquiry | Set thermal acceptability voting in the questionnaire, ask the subjects whether the current thermal environment is acceptable (for example, 1 means acceptable, −1 is unacceptable), and find the statistical results from the questionnaire | [51,58,61,64,68,72,103] |
Thermal sensation | The middle three values (−1, 0, 1) of the ASHRAE seven-point scale are considered as satisfactory or acceptable, while the thermal sensation votes (−3, −2, +2, +3) are considered as unacceptable | [41,45,57,61,62,66,103,105] |
Thermal expectation | The subjects who voted “unchanged” (the thermal expectation vote is 0) are satisfied with the thermal environment they are in, the proportion of the people to the total population are calculated | [60,110] |
Method | Info. | Ref. |
---|---|---|
Thermal sensation | At a 0.5 °C interval, the proportion of people who feel cold (−2, −3) or warm (2, 3) in the temperature range were calculated and fit as a polynomial curve; the operative temperature between two intersection points of the 20% unsatisfactory straight line and the polynomial curve is considered as the acceptable temperature range | [45,64,69] |
Direct inquiry | Acceptance is voted as +1 for thermally acceptable and −1 for thermally unacceptable, and a regression analysis is performed on the thermally unacceptable rate and operative temperature at intervals of 0.5 °C to find the thermally acceptable temperature range | [61] |
Climate Region | Average Neutral Temperature (°C) | |
---|---|---|
Summer | Winter | |
Severe cold | 24.7 | 22.4 |
Cold | 26.6 | 21.2 |
Hot summer and cold winter | 26.4 | 17.3 |
Hot summer and warm winter | 28.0 | 19.7 |
Year | Location | Climate Region | Season | Adaptive Model | Ref. |
---|---|---|---|---|---|
2017 | -- | Severe cold | -- | tn 1 = 0.121 tout 2 + 21.488 | [21] |
2017 | -- | Cold | -- | tn 1 = 0.271 tout 2 + 20.014 | [21] |
2017 | -- | Hot summer and cold winter | -- | tn 1 = 0.326 tout 2 + 16.862 | [21] |
2017 | -- | Hot summer and warm winter | -- | tn 1 = 0.554 tout 2 + 10.578 | [21] |
2010 | Harbin | Severe cold | Summer | tn 1 = 0.486 tout 2 + 11.802 | [44] |
2022 | Jiaozuo | Cold | Summer | tn 1 = 0.271 tout 2 + 20.014 | [50] |
2017 | -- | Hot summer and cold winter | Winter | tn 1 = 0.709 trm 3 + 8.25 | [59] |
2019 | North | -- | -- | tn 1 = 0.438 tout 2 + 13.56 | [45] |
2020 | Nanjing | Hot summer and cold winter | -- | tn 1 = 0.7954 tout 2 + 4.8167 | [46] |
2014 | Xi’an | Cold | Transition season | tn 1 = 0.3322 tout 2 + 14.263 | [70] |
2015 | Yinchuan | Cold | Summer | tn 1 = 0.33 tout 2 + 18.8 | [103] |
2021 | Temperate continental monsoon climate | Cold | -- | tn 1 = 0.2904 tout 2 + 14.367 | [57] |
2021 | Temperate maritime monsoon climate | Cold | -- | tn 1 = 0.1604 tout 2 + 21.626 | [57] |
2021 | Temperate continental desert climate | Cold | -- | tn 1 = 0.4190 tout 2 + 11.2520 | [57] |
2022 | Shanghai | Hot summer and cold winter | Transition season | tn 1 = 0.3681 tout 2 + 17.986 | [66] |
2022 | Wuhan | Hot summer and cold winter | Transition season | tn 1 = 0.3792 tout 2 + 17.671 | [66] |
2006 | Shanghai | Hot summer and cold winter | Summer | tn 1 = 0.42 tout 2 + 15.12 | [71] |
2013 | Lhasa | Cold | Summer | tn 1 = 0.474 tout 2 + 13.8 | [67] |
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Lin, Y.; Chen, P.; Yang, W.; Hu, X.; Tian, L. A Systematic Review on the Studies of Thermal Comfort in Urban Residential Buildings in China. Energies 2024, 17, 991. https://doi.org/10.3390/en17050991
Lin Y, Chen P, Yang W, Hu X, Tian L. A Systematic Review on the Studies of Thermal Comfort in Urban Residential Buildings in China. Energies. 2024; 17(5):991. https://doi.org/10.3390/en17050991
Chicago/Turabian StyleLin, Yaolin, Pengju Chen, Wei Yang, Xiancun Hu, and Lin Tian. 2024. "A Systematic Review on the Studies of Thermal Comfort in Urban Residential Buildings in China" Energies 17, no. 5: 991. https://doi.org/10.3390/en17050991