基于双分支专家融合记忆网络的大规模配电网净负荷高效预测

Precise and efficient net load forecasting is crucial for Power Distribution Systems (PDS) to address the various challenges posed by the increasing penetration of Renewable Energy Sources (RES). Existing studies on net load forecasting often overlook the characteristic differences (i.e., variable heterogeneity) between loads and Distributed Generation (DG), and the differences in graph structures (i.e., spatio-temporal heterogeneity) caused by changes in node net loads. These studies typically assume constant node properties and use the same model with shared parameters to extract spatio-temporal features for different variables, which limits the representation of the features. To address these challenges, this study proposes a novel model named Dual-branch Experts Fusion Memory Network (DEFMN). Specifically, this model designs customized experts (feature branch) with independent parameters for loads and different types of DG (variable branch) to extract features according to the characteristics of various RES. We also employ shared parameters modules, including series embedding, meta spatial memory, and decoder, to fully capture the spatio-temporal correlations between loads and DG, effectively learning their variable heterogeneity as well as spatio-temporal heterogeneity. Additionally, we propose a new model named load-DG coupling model, which aims to construct a novel large-scale PDS with different RES penetration based on the IEEE 8500-node test feeder. Extensive simulations have been conducted on various challenging scenarios (i.e., PDSs with varying RES penetration), and the proposed DEFMN consistently achieves state-of-the-art performance in terms of accuracy (MAPE, etc.) and effectiveness in these scenarios.