Energy consumption prediction and household feature analysis for different residential building types using machine learning and SHAP: Toward energy-efficient buildings

Abstract

U.S. residential buildings account for a significant share of national energy consumption, highlighting their potential for energy-savings. Accurately predicting building energy consumption and understanding the impact of household features are, therefore, crucial for effective energy management, conservation efforts, and the development of energy policies. However, most existing models predicting U.S. residential energy consumption tend to focus on particular regions, limiting their generalizability across the entire country. In addition, many studies have overlooked the significant variations in the drivers of energy consumption between different types of residential buildings, resulting in a lack of separate prediction models for different residential building types. Moreover, when analyzing the impact of household features on building energy consumption, most studies provide a holistic measure of feature importance without sufficient interpretability. To address these gaps, this study uses the Residential Energy Consumption Survey dataset and three tree-based machine learning algorithms to develop separate energy use intensity prediction models for two typical U.S. residential building types. The results demonstrate that the LightGBM-based prediction model performs best for apartments, while the CatBoost-based prediction model performs best for single-family houses. Furthermore, the study applied SHapley Additive exPlanations to analyze the impact of household features on energy consumption. The results reveal that total square footage, space heating with natural gas, climate conditions, and building age are the common key features influencing energy use intensity for both building types. Based on these findings, this study provides general and targeted energy-saving recommendations for both building types, serving as a valuable guide for new building design and retrofitting of existing buildings.