STGCformer: Spatio-Temporal Graph Convolutional Transformer for Short-Term Wind Power Forecasting

The accuracy of short-term wind power forecasting (STWPF) is crucial for the stable operation of power systems. To address the issue of insufficient capture of spatio-temporal dependencies in existing models, which leads to low prediction accuracy, this paper proposes a novel Transformer-based spatio-temporal graph convolutional (STGCformer) model. The time series decomposition module (TSDM) captures periodic fluctuations and long-term variations within the data by performing seasonal trend decomposition. The spatio-temporal graph convolutional (STGC) architecture combines a Graph Attention Network (GAT) with convolutional layers (Convs) to capture both spatial and temporal dependencies, jointly processing the spatio-temporal characteristics inherent in wind power data. The Transformer’s attention mechanism simultaneously handles both short-term and long-term fluctuations. Extensive experimental results show that STGCformer achieves the best prediction accuracy across multiple time steps (24, 48, 72, 96 h), with the average absolute error (MAE) and mean absolute percentage error (MAPE) at 48 h being 41.383 and 3.862, respectively. This model provides a new methodological framework for STWPF.