Energies, Vol. 19, Pages 254: GSTAformer: Graph-Guided Spatio-Temporal Autoformer for Mid-Term Wind Power Forecasting
Energies doi: 10.3390/en19010254
Authors:
Shi Yuan
Yulu Mao
Chenyu Tian
Fang Yu
Tengyue Guo
Min Xia
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.