GCMWSG-HOA-LightGBM: A Hybrid Load Prediction Method for Intermittent HVAC Systems with Boundary Feature Protection

Forecasting and intelligent operation of HVAC systems in public buildings are crucial for energy conservation, carbon reduction, and enhancing occupant comfort. This area has seen significant progress. However, for intermittent HVAC systems, noise reduction techniques applied to periodic load sequences may distort the load during start–stop transitions. To tackle this challenge, an innovative hybrid prediction method, GCMWSG-HOA-LightGBM, is proposed in this study. The GCMWSG filter utilizes period division and boundary protection mechanisms to avoid cross-period coupling errors in traditional filters, preserving the authenticity of cooling load start–stop transitions. Additionally, the HOA is employed to automatically optimize the hyperparameters of the LightGBM model, enhancing both parameter tuning efficiency and prediction accuracy. The proposed model is validated using operational data from an office building in Foshan, Guangdong Province, and its performance is compared with several hybrid models: HOA-LightGBM, BWO-LightGBM, AO-LightGBM, TPE-LightGBM, HOA-RF, BWO-RF, AO-RF and TPE-RF. The results show that the GCMWSG-HOA-LightGBM model outperforms all comparison models across all metrics, achieving R2, MAPE, and CVRMSE values of 92.22%, 7.05%, and 9.46%, respectively, highlighting its accuracy and stability. The findings provide an efficient solution for intelligent load prediction of HVAC systems with intermittent and periodic characteristics, offering valuable engineering insights and theoretical guidance.