GSTAformer: Graph-Guided Spatio-Temporal Autoformer for Mid-Term Wind Power Forecasting
Abstract
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.
Fields of Study & Subjects
Energy Load and Power Forecasting
Solar Radiation and Photovoltaics
Time Series Analysis and Forecasting
Publication details
- Journal / source
- Energies
- Publisher
- MDPI AG
- Volume
- 19
- Issue
- 1
- Pages
- 254–254
- Publication date
- 2 January 2026
- Language
- EN