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, forming a strong foundation for reliable longitudinal analysis.
Random Forest Modeling and Predictive Stability
A Random Forest regression model was adopted due to its robustness against nonlinearity and multicollinearity in complex building energy systems. The model achieved stable predictive performance with R² values exceeding 0.8, demonstrating strong generalization capability. By comparing models incorporating temporal features with those excluding them, the study identifies systematic slow-varying residual trends indicative of gradual performance degradation.
Residual-Based Aging Diagnosis Methodology
The core innovation of the study lies in its residual-based diagnostic approach. Persistent positive residual trends over time reveal an increase in HVAC energy consumption beyond expected climatic and occupancy influences. This method quantitatively captures envelope and system performance deterioration, providing empirical evidence of aging that is otherwise difficult to detect through conventional short-term analyses.
Thermal Response Lag and Seasonal Variability
A one-day lag analysis further demonstrates a gradual weakening of the building’s energy response to temperature fluctuations. This phenomenon suggests increased thermal inertia and possible degradation of insulation performance. The impacts differ between cooling and heating seasons, reflecting seasonal sensitivity in envelope aging characteristics and HVAC operational behavior.
Implications for Lifecycle Retrofit and Data Requirements
The study establishes that at least five consecutive years of operational data are necessary to ensure model stability and trend validity for aging diagnosis. The proposed methodology provides a transferable, scalable framework for lifecycle-oriented performance evaluation. By enabling early detection of envelope degradation and system inefficiencies, this approach supports evidence-based retrofit decisions and enhances long-term resilience of building energy systems under real operational conditions.
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