Thermal preference prediction based on occupants’ adaptive behavior in indoor environments- A study of an air-conditioned multi-occupancy office in China

Abstract

Exploring new thermal preference prediction models or methods to precisely analyze occupants’ unconscious feedback on the thermal environment without disturbing them is essential for increased building efficiency, comfort and productivity. In this study, we propose a novel method for developing a comfort model that uses occupant self-adjusting behavior to predict thermal preference. The model development draws from field data including thermal self-adaptive behaviors, environmental parameters, and thermal preferences collected from 34 occupants in a single multi-occupancy room in a research office in a university in south-east China, and it employs four machine learning algorithms including Support Vector Machine, Random Forest, K-Nearest Neighbor, and Decision Tree. The results indicate that there is a close relationship between thermal preference and thermal self-adaptive behavior. The highest prediction accuracy of thermal preference using a single set of input parameters is 0.81, obtained using self-adaptive behaviors. When the input parameters increase to four sets or more, the highest median accuracy of thermal preference prediction (0.85) does not change significantly. When it is difficult to obtain personal information (gender, height, and weight) and data on clothing thermal resistance, it is recommended to use a combination of three sets of parameters (self-adaptive behavior, indoor temperature and relative humidity (T/RH), and outdoor T/RH) or a combination of four sets of parameters (self-adaptive behavior, indoor T/RH, outdoor T/RH, and globe temperature) as the input parameters of the thermal preference prediction model based on Random Forest, which results in an expected prediction accuracy of 0.84 and 0.85, respectively.