A personalized regression model for predicting thermal sensation based on local skin temperature in moderate summer conditions

Abstract

Personalized thermal sensation models play a crucial role in ensuring occupant's thermal comfort satisfaction and improving building energy efficiency. However, an adaptive and accurate personalized model that can be easily implemented in real life is still challenging. This paper investigates the influencing factors of thermal sensation vote (TSV) and proposes a personalized regression model that only uses a single local skin temperature as the key indicator. A survey is conducted with forty subjects aging from 20 to 59 years old. The relationship among ambient temperature, skin temperatures, and subjective TSV is analyzed. The forehead temperature is recommended as the key indicator for prediction because it exhibits a strong correlation with ambient temperature and TSV, and it is easy to capture. Furthermore, the impact of individual characteristics on TSV is investigated. The proposed model effectively captures and compensates for individual differences by incorporating subjects' set point skin temperature and body fat percentage (BF%). The proposed model can be readily applied in real-life scenarios due to its minimal requirement for occupant’s feedback and its higher accuracy compared to other models. Specifically, it exhibits a significantly lower Root Mean Square Error (RMSE) of 15.8 %, 9.4 %, and 65.2 % compared to the Support Vector Regression (SVR) model Zhang's model and Zhou's model. Moreover, the proposed model showcases the lowest mean absolute error among the compared models. This approach of developing a personalized regression model based on local body temperature holds promise for future international ergonomic standard development.