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Heating and cooling in buildings accounts for over 20% of total energy consumption in China. Therefore, it is essential to understand the thermal requirements of building occupants when establishing building energy codes that would save energy while maintaining occupants' thermal comfort. This paper introduces the Chinese thermal comfort dataset, established by seven participating institutions under the leadership of Xi'an University of Architecture and Technology. The dataset comprises 41,977 sets of data collected from 49 cities across five climate zones in China over the past two decades. The raw data underwent careful quality control procedure, including systematic organization, to ensure its reliability. Each dataset contains environmental parameters, occupants' subjective responses, building information, and personal information. The dataset has been instrumental in the development of indoor thermal environment evaluation standards and energy codes in China. It can also have broader applications, such as contributing to the international thermal comfort dataset, modeling thermal comfort and adaptive behaviors, investigating regional differences in indoor thermal conditions, and examining occupants' thermal comfort responses. [ABSTRACT FROM AUTHOR]

Reducing the energy consumption of large shopping malls is beneficial to achieving China's dual carbon goals. This study aims to explore the thermal comfort demands of occupants and provide guidance for the energy-efficient design of the indoor thermal environment in shopping malls. We selected nine typical large shopping malls in the cold area of China as the research object and carried out a year-long field study, which involved the survey of physical environment parameters and subjective questionnaires. 3175 valid data sets were obtained in all seasons. The study results found that the indoor thermal environment suffers from overheating in winter and overcooling in summer. The neutral T op is 21.3 °C, 25.4 °C, 19.0 °C, and 17.4 °C in spring, summer, autumn, and winter, respectively. The upper limit of the 80% temperature acceptability range in summer was 27.5 °C. The 80% acceptability range in winter (16.4–23.2 °C) is wider than GB 50736 standard, which can be appropriately relaxed for the HVAC temperature setting in the mall. Apparent vertical thermal stratification exists in the malls and significantly affects the occupants' thermal comfort. Compared to the PMV model, the adaptive model was more applicable for thermal environment evaluation of shopping malls in transition season and summer. This study proposes indoor temperature settings under the premise of meeting human thermal comfort, which provides references for the design and operation of shopping malls in cold-climate zones. • The indoor thermal environment of the shopping malls is overheating in winter and overcooling in summer. • The respondents expressed a high proportion of thermal acceptability and higher air quality acceptability for the thermal environments. • The neutral T op was 21.3 °C, 25.4 °C, 19.0 °C and 17.4 °C in spring, summer, autumn, and winter, respectively. • It can be appropriately relaxed for the HVAC temperature setting in winter. • There is significantly vertical thermal stratification in the shopping malls. [ABSTRACT FROM AUTHOR]

Adjusting clothing for changing climates is an effective way for the perseverance of human thermal comforts. In this study, the clothing insulation (I clo) of occupants and their thermal adaption to cold climates are discussed based on the Chinese Thermal Comfort Database. Particularly, the raw data was selected from five climate zones of China: severe cold (SC), cold, hot summer and cold winter (HSCW), hot summer and warm winter (HSWW) and Temperate regions. Statistical approaches were adopted to understand how indoor and outdoor air temperatures, types of climate zones and buildings play roles in the clothing behaviour of occupants. Finally, a linear mixed model of I clo was proposed by inputting the most influential numerical variable and categorical variables, i.e., indoor air temperature (t a), tags of climate zones and building types. The result indicated that t a in the northern parts of China where district heating is employed were perceivably higher, compared to that in HSCW and Temperate areas with separate heating systems indoors. Besides, the clothing behaviour correlated greater with indoor air temperature than outdoor air temperature, as the database incorporated environmental parameters in civil buildings where people stayed indoors most of time. The I clo when individuals were in thermal comfort significantly differed from that when they were uncomfortable. So, comfortable clothing insulation was further analysed to establish the model of I clo. The model gives an explanation of how individuals' clothing behaviour interact with t a in different building contexts, thus providing a support for indoor environmental design. • Factors influencing the clothing insulation of occupants were discussed. • Indoor air temperature of civil buildings impacted more on clothing than outdoors. • Irregular indoor environments stimulate occupants' adaptive behaviour. • A comprehensive model of clothing insulation was established and validated. [ABSTRACT FROM AUTHOR]

Thermal comfort evaluations in the built environment are essential to occupant's satisfaction and also building energy consumption. Traditionally, thermal comfort has been assessed by Fanger's PMV model that was developed based on extensive laboratory tests. However, it's been found that PMV predictions are not good in field studies, especially in buildings with natural ventilation. In this paper, machine learning (ML) algorithms were used to predicted occupants' thermal comfort votes (TCV) and thermal sensation votes (TSV), using 5512 sets of thermal comfort data collected in naturally ventilated residential buildings in fourteen cities in China. Environmental parameters, personal parameters, climatic types, and adaptive control measures were considered and used as input parameters for the ML model. It was found that environmental parameters (both outdoors and indoors), personal parameters (metabolic rate and clothing insulation), and climatic types all significant affect both TCV and TSV, while adaptive control measures only affect TSV but not TCV. Comparing with established models (PMV, ePMV and aPMV), the ML models had smaller errors in predicting TSV and TCV. Based on these results, we suggest that the ML, especially ANNs model was reliable in predicting occupants' TCV and TSV in naturally ventilated residential buildings, and performed better than traditional thermal balance based models. Moreover, using the new model, we found that acceptable temperature ranges in naturally ventilated buildings were far wider than the ASHRAE adaptive comfort zone, suggesting that appropriate models should be established based on local data. [ABSTRACT FROM AUTHOR]

• A novel index is proposed to analyse quantitatively the local climate potential of free-running buildings. • The summer climate potential of free-running buildings in Harbin, Beijing, Wuhan, Kunming, and Guangzhou was found to be 90%-98%, 83%-96%, 75%-88%, almost 100%, and 62%-93%, respectively. • Cities in Northern China are better suited for passive cooling design due to higher summer climate potential. • There is a decrease pattern in the summer climate potential across China from 1971 to 2012. This study gathered hourly temperatures and solar radiation data from 42 years for walls in five cities within the five major climate zones across China. The long-term hourly operative temperatures in summer were determined in accordance with the ISO standard. A novel index is proposed to analyse quantitatively the local climate potential of free-running buildings. The index calculates the number of hours when the indoor operative temperature is above/below the acceptable upper limit of thermal comfort, counted in summer or throughout a year. In Harbin, Beijing, Wuhan, Kunming, and Guangzhou, the summer climate potential of free-running buildings was found to be 90 %-98 %, 83 %-96 %, 75 %-88 %, almost 100 %, and 62 %-93 %, respectively. Cities in Northern China are better suited for passive cooling design because they have a diminishing pattern of climate potential from colder climates in the north to warmer climates in the south. Additionally, positive slopes were found for the long-term shifting patterns of hour proportion from 1971 to 2012, pointing to a decrease in the summer climate potential across China. These findings have significant effects on the analyses of how buildings can adapt to and mitigate climate change and the likely changes in energy use. [ABSTRACT FROM AUTHOR]

Providing comfortable indoor thermal conditions in educational buildings is essential for students' learning performance and general satisfaction. This study investigates seasonal variations in thermal environment and comfort in classrooms in the cold area in China through year-long field surveys, which involve concurrent indoor thermal conditions measurement and students' subjective responses surveys. Overall, 1973 university students were surveyed. Distinct seasonal differences were observed in room temperature, relative humidity, and CO 2 concentration, resulting in significant differences in neutral temperatures for each season. The neutral temperatures in summer and winter are 26.2 °C and 22.4 °C, respectively. The 80% of the acceptable temperature range was 21.3–25.4 °C in winter, and the upper acceptable temperature limit in summer was 29.6 °C. Windows and doors were opened less during cold conditions with low indoor and outdoor temperatures, which resulted in the indoor CO 2 concentration level increased to 2500 ppm. Compared to the PMV, the scissors difference exists in non-air-conditions environments in spring, summer, and autumn but can accurately predict the central heating environments in winter. And the adaptive thermal comfort method fits better with the subjects' thermal comfort. Overall, it is suggested that for classrooms in cold areas in China, cooling is needed in summer when room temperature exceeds 30 °C, while proper ventilation shall be provided in winter. [ABSTRACT FROM AUTHOR]

Indoor thermal environment design parameters have a significant impact on human thermal comfort, health and building energy consumption. The methods of determining indoor thermal environment design parameters in China and around the world are mainly based on the PMV model. This method often results in certain errors deviations from the actual situation and has difficulty reflecting the differences in the thermal environmental requirements in different climate regions, seasons, building types, etc. This paper proposes a data-driven method based on Chinese Thermal Comfort Database and uses a logistic regression to obtain the indoor air temperature range that 80% of occupants find thermally acceptable under different conditions. The results indicate that the prediction of this model are in good agreement with the actual data and have as higher accuracy in predicting the percentage of thermal acceptability than a linear regression method. This model shows that different regions, seasons, and building types have different acceptable temperature ranges and that the acceptable air temperature ranges are on average 2.0 °C wider than the ISO 7730 and 0.7 °C wider than the GB50736-2012. This method is conducive to the determination of air temperature thresholds and better reflects people's equirements for thermal environment under different conditions. [ABSTRACT FROM AUTHOR]