Day-Ahead Net Load Forecasting for Renewable Integrated Buildings Using XGBoost

With the large-scale adoption of photovoltaic (PV) systems as a renewable energy source, accurate long-term forecasting benefits both utilities and customers. However, developing forecasting models is challenging due to the need for high-quality training data at fine time intervals, such as 15 and 30 min resolutions. While sensors can track necessary data, careful analysis is required, particularly for PV systems, due to weather-induced variability. Well-developed forecasting models could optimize resource scheduling, reduce costs, and support grid stability. This study demonstrates the feasibility of a day-ahead net load forecasting model for a mixed-use office building. The model was developed using multi-year campus load and PV data from the University of Hawaii at Manoa. Preprocessing techniques were applied to clean and separate the data, followed by developing two decoupled models to forecast gross load demand and PV production. A weighted-average function was then incorporated to refine the final prediction. The results show that the model effectively captures day-ahead net load trends across different load shapes and weather conditions.