Abstract

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.

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