TSG‐Net: A Multiscale Decomposition and Spatio‐Temporal Graph Neural Network Framework for High‐Precision Wind Power Forecasting

ABSTRACT Wind energy's intermittency poses significant challenges for power grid stability. Existing forecasting methods exhibit notable limitations: traditional machine learning models struggle with long‐term temporal dependencies, while deep learning approaches often overlook spatial relationships among turbines. This paper proposes TSG‐Net, a novel framework integrating multiscale decomposition, graph neural networks, and attention mechanisms. TSG‐Net employs three innovative components: a multiscale decomposition linear network (MDLinear) with learnable decomposition weights for adaptive temporal feature extraction, a spatio‐temporal graph network (XTGN) that constructs dynamic adjacency matrices based on real‐time wind directions for wake effect modeling, and an adaptive attention fusion mechanism for scenario‐specific weight allocation. Experiments on the SDWPF dataset demonstrate that TSG‐Net achieves MAE of 36.53 kW and RMSE of 45.17 kW, representing improvements of 8.3% and 5.7%, respectively, compared with the best baseline TSB‐GNN (39.85‐kW MAE, 47.92‐kW RMSE), with particularly strong performance during wind direction changes, extreme weather, and power ramp events.