A machine learning led investigation to understand individual difference and the human-environment interactive effect on classroom thermal comfort

Abstract

The availability of the global thermal open database means that machine learning models have been increasingly applied in thermal comfort studies in order to understand the factors and mechanisms that affect human thermal sensation. Previous global database analyses focused less on classroom thermal comfort, however, and more on model accuracy, while model interpretation was usually ignored, and individual differences and interaction effects are particularly poorly explained. This study screened 4527 related records about classrooms from the ASHRAE Global Thermal Comfort Database II, and used the cleaned data to train a hybrid model of extreme gradient boosting (XGBoost) and Bayesian optimisation (BO). SHAP values were used to interpret the machine learning model. The results identified ten key influencing factors that are associated with thermal comfort, although their importance varies among individuals. The effects of the factors can also be divided into main effects (80%) and interactive effects (20%), and some interactive effects are more potent than the main effect. Three typical types of interactive effects are concluded: two-way interaction, one-way interaction, and cross-interaction. This study was based on a comprehensive global database and an innovative machine learning method, and will lead to a more robust personal comfort model (PCM) that guides HVAC design and regulation development in order to meet thermal environment and energy-saving requirements.