A Hybrid Data-Driven and Knowledge-Driven Method for Commercial HVAC Load Identification

Accurate HVAC load identification from low-frequency smart-meter data is important for commercial-building demand response and energy management, but remains difficult when high-frequency measurements, detailed physical models, and HVAC submeters are unavailable. This paper proposes a hybrid data-driven and knowledge-driven framework that integrates HVAC load disaggregation with day-ahead forecasting. Operating modes are first identified from normalized daily load shapes and calendar features using K-medoids clustering. For each mode, a non-HVAC baseline is constructed from mode-wise low-load observations, cyclic smoothing, and a label-free shape correction based on the representative operating profile and lower-tail load dispersion. HVAC load is then obtained as the physically constrained residual between whole-building load and the corrected baseline. Historical disaggregation estimates are subsequently used as pseudo-labels for a leakage-controlled Extra-Trees forecasting model that combines target-day weather and calendar information with admissible historical load features. Experiments on three 15 min NREL ComStock commercial-building datasets show that the proposed disaggregation method achieves R2 values of 0.8308–0.9246 across building types, while the proposed forecasting model attains an R2 of 0.7812 on the held-out test period. The results demonstrate an interpretable and submeter-free approach for HVAC load analysis under low-frequency metering, with the strongest performance under cooling-dominated conditions.