Data-Driven Diagnosis of Building Performance Aging and Its Impact on HVAC Energy Consumption
Accurate forecasting of building energy consumption is essential for achieving carbon neutrality, enhancing operational efficiency, and maintaining long-term building performance. While significant attention has been given to short-term prediction models, the progressive effects of building performance aging particularly on HVAC energy consumption—remain insufficiently quantified. This study addresses this gap by developing a robust data-driven methodology to diagnose aging-related performance degradation using long-term operational datasets. Long-Term Dataset and Integrated Analytical Framework The research is based on ten years (2015–2024) of continuous operational data from a university educational building in Chongqing, China. The dataset integrates sub-metered HVAC energy records, detailed meteorological observations, and occupancy-related proxy variables. This comprehensive data fusion enables the isolation of performance aging effects from climatic and behavioral variability,...