Human thermal comfort prediction in residential buildings based on decision tree model

The established variables reduce the prediction accuracy of traditional thermal comfort models, while the models such as artificial neural network limit to be explained well due to their complicated operation rules. Presents a typical classification tree—C&RT model to analyse the occupants’ thermal comfort and the related influencing factors. Based on the field survey of free-running residential buildings in six cities (Chengdu, Chongqing, Wuhan, Nanjing, Changsha, Hangzhou) in hot summer and cold winter zone, develops a decision tree model of thermal sensation by choosing the city as the first classification feature. The results show that the obtained decision tree model has good performance to identify the key factors affecting on thermal comfort and explain the internal logic between these factors, which provides a new approach for predicting thermal comfort and designing thermal environments.