Chenyu Tian

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.

Accurate wind power forecasting is crucial for modern power systems, yet most deep learning models neglect spatial relationships between turbines. We propose GSTAformer, a graph-guided spatio-temporal model capturing both spatial and temporal dependencies through MIC- and PCC-built graphs; GraphSAGE for spatial feature extraction; multi-scale convolution for trend detection; and an improved Autoformer for temporal modeling. Experiments on SDWPF and GEFCom2012 datasets demonstrate GSTAformer’s superior performance, achieving a 24 h mean squared error (MSE) of 0.7480 and mean absolute error (MAE) of 0.6362 on SDWPF. This work integrates graph-based spatial modeling with enhanced temporal forecasting for medium-term wind power prediction, providing a coherent framework suited to complex wind energy scenarios.