A Multi-Scale Spatial–Temporal Graph Neural Network-Based Method of Multienergy Load Forecasting in Integrated Energy System

Accurately predicting multi-energy loads is essential for optimizing the dispatch and economic operation of integrated energy systems (IES). However, existing multi-energy load forecasting methods have two main limitations: (1) they fail to consider the complex correlations between multi-energy loads and auxiliary features; (2) single time-scale feature extraction methods can result in the loss of critical temporal feature information. Therefore, multi-energy load forecasting remains a challenging task. To overcome these limitations, this paper proposes a novel multi-energy load forecasting method based on a multi-scale spatio-temporal graph neural network (MS-STGNN). Specifically, the proposed continuous graph learning module quantifies the correlations between multi-energy loads and auxiliary features using an adjacency matrix, while the graph convolution module aggregates feature information among neighboring nodes through the same matrix to improve the correlations between multi-energy loads and auxiliary features. The model's robustness is further enhanced by the feature attention module. Moreover, to mitigate temporal feature information loss, we develop a multi-scale convolution module that uses filters of various sizes to extract multi-dimensional temporal features of different time steps. Extensive experiments show that the MS-STGNN method has higher prediction accuracy and better generalization ability than existing methods on the IES dataset at the Tempe campus of Arizona State University. The code is publicly available at https://github.com/nuist-cs/MS-STGNN .