An Explainable Evaluation Model for Building Thermal Comfort in China

Abstract

The concentration of atmospheric greenhouse gases is being amplified by human activity. Building energy consumption, particularly for heating and cooling purposes, constitutes a significant proportion of overall energy demand. This research aims to establish a smart evaluation model to understand the thermal requirements of building occupants based on an open-access dataset. This model is beneficial for making reasonable adjustments to building thermal management, based on factors such as different regions and building user characteristics. Employing Bayesian-optimized LightGBM and SHAP (SHapley Additive exPlanations) methods, an explainable machine learning model was developed to evaluate the thermal comfort design of buildings in different areas and with different purpose. Our developed LightGBM model exhibited superior evaluation performance on the test set, outperforming other machine learning models, such as XGBoost and SVR (Support Vector Regression). The SHAP method further helps us to understand the interior evaluation mechanism of the model and the interactive effect among input features. An accurate thermal comfort design for buildings based on the evaluation model can benefit the carbon-neutral strategy.

Keywords

• thermal comfort
• building energy consumption
• machine learning
• open-access dataset