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This study investigated the effects of four important microclimate parameters on outdoor thermal sensation and neutral temperature. A long term (almost 2 years) field investigation was conducted at six typical public spaces in Changsha China, with hot-summer and cold-winter climate. During a single investigation of one day (from 9:00 to 18:00), outdoor air temperature, air humidity, global temperature and wind speed were continuously measured at one measuring point. People visiting the public spaces (near the measuring site) were randomly invited to fill out a questionnaire describing their feelings of thermal comfort and other important psychological factors. Based on the field data (7851 samples), relationship between people’s outdoor thermal sensation and the microclimate parameters were quantified. Further, the effect of the microclimate parameters on outdoor thermal sensation and neutral temperatures was analyzed. The results revealed that outdoor microclimate parameters, especially outdoor air temperature, play important roles on outdoor thermal sensation when people stay outdoors. The variation in outdoor microclimate parameters led to the biggest change of outdoor neutral temperature in winter. Seasonal and regional differences in outdoor neutral temperature were found, which reflects different thermal comfort requirements under distinct outdoor microclimate conditions. [ABSTRACT FROM AUTHOR]

• A dynamic setting temperature that adapts to climate change was proposed. • The method was based on clothing adjustment behavior which adapts to thermal comfort. • The dynamic setting temperature had significant energy saving potential. • The dynamic setting temperature is useful for intelligent control of air-conditioner. Saving energy consumption of air-conditioning is essential to building energy conservation while satisfying the thermal comfort requirements of indoor environment. On the basis of human clothing adjustment behavior, a dynamic setting temperature (DST) that adapts to climate change was proposed in this paper, i.e. indoor air-conditioning setting temperature changes dynamically with outdoor air temperature; furthermore. A method was established to obtain the DST. The daily mean clothing insulation was predicted according to the historical temperature at first, then indoor thermal comfort temperature was calculated based on the variable clothing insulation, and finally the DST was determined. According to the value or range of predicted mean vote (0, -0.5~0.5, -1~+1) corresponding to the thermal comfort, three levels of DST were defined. The corresponding thermal acceptability rates were 95%, 90% and 73%, respectively. A simulation of DST was conducted in a public building in Changsha, China based on meteorological data of 2017. Three levels of DST ranges were obtained, varied from 20.1 to 27.8 °C in level I, 17.8 to 28.7 °C in level II and 15.4 to 29.7 °C in level III, respectively. The simulation results of operational air-conditioning energy consumption shows that the application of DST could save up to 65.5% of energy consumption compared with the traditional fixed setting temperature (FST), and the operational energy consumption decreased by 59.1% due to the application of level-III DST compared with level-I DST. Therefore, the DST proposed in this paper has significant energy saving potential while meeting the thermal comfort requirements. [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]