Differential Effects of Building Design Parameters on Building Energy Performance in the Western Sichuan Plateau: An Explainable Machine Learning Analysis

Building energy use in the Western Sichuan Plateau exhibits strong seasonal variability owing to its cold climate and intense solar radiation. The objective of this study is to quantify and interpret the contributions of building design parameters to heating, cooling, and total energy use in the Western Sichuan Plateau by integrating parametric energy simulation with explainable machine learning. First, we analyzed the existing hourly energy consumption data of a specific building. Subsequently, 62,208 parametric building schemes were simulated using EnergyPlus (version: 22.1.0) by varying geometry, orientation, window-to-wall ratio (WWR), and envelope characteristics. XGBoost models were developed to predict heating demand, cooling demand, and total energy use intensity (EUI), achieving R2 values of 0.9998, 0.9997, and 0.9998, respectively, with MAPE values of 0.45%, 2.75%, and 0.26%. SHAP analysis revealed clear target-dependent effects of design parameters. SHAP analysis revealed target-dependent differences in the importance of design parameters. Wall construction, orientation, and building length were the top three contributors to heating EUI; orientation, wall construction, and WWR ranked highest for cooling EUI; and wall construction, building length, and orientation were most influential for total EUI. Increasing WWR generally reduced its SHAP values for heating EUI, with a weaker effect observed for the insulated-wall configuration, whereas its SHAP values for cooling EUI generally increased. Total EUI showed an overall decreasing trend with increasing WWR. These findings highlight the importance of evaluating building design parameters in relation to heating, cooling, and total energy performance. The identified nonlinear and target-dependent response patterns provide insights into climate-responsive building design in the Western Sichuan Plateau.