Machine Learning-based Prediction of Indoor Thermal Comfort in Traditional Chinese Dwellings: A Case Study of Hankou Lifen

Abstract

Traditional residential dwellings as architectural heritage holds substantial research value and significance for regional studies. This study investigates the indoor thermal comfort of traditional "Hankou Lifen" dwellings compared to new residences during summer through field measurements and questionnaires. The results demonstrate that the indoor thermal environment of "Lifen" houses is inferior, leading to lower thermal comfort for the residents. A linear regression model assesses summer residents' thermal comfort, considering psychological perspectives to calculate natural and acceptable temperature ranges. However, these models primarily rely solely on temperature and overlook other environmental and personal factors, due to their lack of accuracy in capturing complex non-linear relationships. To address these limitations, six machine learning models with high predictive performance are selected for analyses. Data preprocessing techniques, such as upsampling, and hyper-parameter optimization are employed to enhance prediction performance. The selection of algorithms is based on metrics such as accuracy, precision, recall, and F1 score, with further performance evaluation conducted using ROC curves and confusion matrices. Through evaluation, XGBoost and LightGBM show superior performance in predicting residential thermal comfort.Finally, SHAP(SHapley Additive exPlanations) values and PDP(Partial Dependence Plot) are used to interpret the models, revealing various factors influencing residents' thermal sensations in both new and old dwellings. These findings emphasize the significance of thermal comfort in the preservation and revitalization of historic dwellings and propose a novel framework for advancing the sustainable utilization of traditional dwellings.