Trustworthy Data-Driven Hybrid Modeling of Building Energy Performance and Greenhouse Gas Emissions

Abstract

Reducing carbon dioxide (CO2) emissions from buildings is essential for climate change mitigation, with universities representing major energy consumers. This study develops a hybrid data-driven framework combining machine learning and simplified emission factor rescaling to predict campus-wide CO2 emissions. Nine machine learning models were comparatively evaluated under both cross-sectional and temporal validation settings. Among all evaluated models, the Artificial Neural Network (ANN) demonstrated the most reliable predictive performance, achieving the best balance between prediction accuracy and generalization capability. Although the proposed physics-informed LSBoost_PI framework aimed to integrate physical priors with machine learning through residual correction, it did not improve predictive generalization under the limited sample conditions of the dataset. Time-series cross-validation further confirmed the ANN model's temporal forecasting capability (RMSE = 2.13 ton/year, R-2 = 0.985). To support trustworthy and interpretable machine learning, feature importance analysis identified CO2 intensity indicators (CO2/kWh and CO2/TEP) as the dominant drivers of emissions. The study also conducted an emission reduction assessment, revealing that a limited number of high-energy buildings dominate overall campus emissions. These findings provide actionable insights for campus-scale energy management, supporting targeted energy efficiency improvements and renewable energy integration strategies in high-emission buildings.