Sparse Bayesian Learning Based Hybrid Method for Load Forecasting in Integrated Energy Systems

In integrated energy systems, load forecasting has become increasingly significant on the background of carbon peaking and carbon neutrality. This study aims to apply a probabilistic learning method, i.e. sparse Bayesian learning, to achieve day-ahead and week-ahead load forecasting. Different from previous studies, the proposed method integrates multiple regression with SBL method by using it as a weather forecasting step while the whole learning and prediction process is based on feature engineering and moving window techniques. Test cases are conducted on historical load data from New York Independent System Operator (NYISO). In terms of prediction performance, the SBL-based hybrid method outperforms single relevance vector machine (RVM), artificial neural network and multiple regression method on load forecasting.