Short-Term Residential Load Forecasting Framework Based on Spatial-Temporal Fusion Adaptive Gated Graph Convolution Networks.

Abstract

Enhancing the prediction of volatile and intermittent electric loads is one of the pivotal elements that contributes to the smooth functioning of modern power grids. However, conventional deep learning-based forecasting techniques fall short in simultaneously taking into account both the temporal dependencies of historical loads and the spatial structure between residential units, resulting in a subpar prediction performance. Furthermore, the representation of the spatial graph structure is frequently inadequate and constrained, along with the complexities inherent in Spatial-Temporal data, impeding the effective learning among different households. To alleviate those shortcomings, this article proposes a novel framework: Spatial-Temporal fusion adaptive gated graph convolution networks (STFAG-GCNs), tailored for residential short-term load forecasting (STLF). Spatial-Temporal fusion graph construction is introduced to compensate for several existing correlations where additional information are not known or unreflected in advance. Through an innovative gated adaptive fusion graph convolution (AFG-Conv) mechanism, Spatial-Temporal fusion graph convolution network (STFGCN) dynamically model the Spatial-Temporal correlations implicitly. Meanwhile, by integrating a gated temporal convolutional network (Gated TCN) and multiple STFGCNs into a unified Spatial-Temporal fusion layer, STFAG-GCN handles long sequences by stacking layers. Experimental results on real-world datasets validate the accuracy and robustness of STFAG-GCN in forecasting short-term residential loads, highlighting its advancements over state-of-the-art methods. Ablation experiments further reveal its effectiveness and superiority.