Physics-Informed Series-Aware Graph Transformer Model for Net Load Forecasting

Abstract

The growing integration of renewable energy sources (RESs), such as photovoltaic (PV) and wind (WD), has significantly increased the variability of net load (NL), posing critical challenges on the net load forecasting. In this paper, a novel physics-informed series-aware graph Transformer (PISAGT) model is proposed for the net load forecasting, which synergistically combines the advantages of both direct and indirect forecasting methods to achieve superior forecasting performance. Firstly, a physics-informed loss module (PILM) is proposed, which introduces the physical characteristics of the net load into the loss function to incorporate physical knowledge in the gradient descent optimization, thereby enhancing the model’s generalization capability for more stable and trustworthy predictions. Secondly, a variable-scale patch embedding method segments time series into subsequence-level patches and transforms them into 2D representations, which enables the forecast model to simultaneously capture local temporal patterns across diverse forecasting horizons. Thirdly, a series-aware graph learning mechanism (SAGLM) which includes graph token encoding, adaptive adjacency matrix learning, and graph convolution, is proposed to integrate graph neural networks (GNNs) with Transformer within a synergistic framework to comprehensively capture intra-series and inter-series dependencies in net load datasets. Finally, case studies on the real-world datasets demonstrate that the PISAGT model can achieve a 14.4% improvement in prediction accuracy compared with direct forecasting methods, showing its effectiveness in net load forecasting.