Online Ensemble Learning for Load Forecasting

Abstract

Traditionally, load forecasting models are trained offline and generate predictions online. However, the pure batch learning approach fails to incorporate new load information available in real-time. Conversely, online learning allows for efficient adaptation of newly incoming information. This letter advocates a novel online ensemble learning approach for load forecasting by combining batch and online learning. While the individual batch models provide an appropriate forecast basis, the online ensemble combines their predictions and ensures adaptivity for online application. In that respect, we propose a modified Passive Aggressive Regression (PAR) model to implement the online ensemble forecasting. Case studies on a real-world load dataset show that the proposed method can improve the forecasting accuracy significantly compared to a pure batch learning approach.