Residential Load Forecasting: An Online-Offline Deep Kernel Learning Method

Abstract

Residential load forecasting (RLF) is critical for power system operations. Different from traditional system-level load forecasting, studying RLF faces the challenges of high uncertainty. Besides, learning temporal dynamics within the residential load sequences is important. However, existing methods fail to effectively tackle the fore-mentioned challenges simultaneously. In this article, a deep kernel is proposed by integrating the deep soft Spiking Neural Networks (sSNN), which is then applied to perform Gaussian Process (GP) regression. The constructed regressor investigates the temporal dynamics within the residential load sequence and retains the probabilistic advantages for uncertainty estimates. Furthermore, to better address the high uncertainty of RLF, a learning scheme combing both offline and online learning is specifically developed for the regressor. Such a learning scheme contributes to fully exploring historical information while learning the uncertainty from real-time data. The effectiveness of the proposed method is demonstrated on three public and actual residential load datasets.