Electric Load Forecasting for Individual Households via Spatial-Temporal Knowledge Distillation

Abstract

Short-term load forecasting (STLF) for residential households has become of critical importance for the secure operation of power grids as well as home energy management systems. While machine learning is effective for residential STLF, data and resource limitations hinder individual household predictions operated on local devices. In contrast, utility companies have access to broader sets of data as well as to better computational resources, and thus have the potential to deploy complex forecasting models such as Graph neural network-based models to explore the spatial-temporal relationships between households for achieving impressive STLF performance. In this work, we propose an efficient and privacy-conservative knowledge distillation-based STLF framework. This framework can improve the STLF forecasting accuracy of lightweight individual household forecasting models via leveraging the benefits of knowledge distillation and graph neural networks (GNN). Specifically, we distill the knowledge learned from a GNN model pre-trained on utility data sets into individual models without the need to access data sets of other households. Extensive experiments on real-world residential electric load datasets demonstrate the effectiveness of the proposed method.