GSTAformer: Graph-Guided Spatio-Temporal Autoformer for Mid-Term Wind Power Forecasting
Nanjing University of Information Science and Technology · Guizhou Electric Power Design and Research Institute · +1 more institution
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…
Citation impact
- FWCI
- 43.76
- Percentile
- 100%
- References
- 0
Authors
6- SYShi Yuan
Nanjing University of Information Science and Technology
- YMYulu Mao
Nanjing University of Information Science and Technology
- CTChenyu Tian
Nanjing University of Information Science and Technology
- FYFei Yu
Guizhou Electric Power Design and Research Institute
- TGTengyue Guo
Nanjing University of Information Science and Technology, University of Reading
Topics & keywords
- Wind power
- Convolution (computer science)
- Mean squared error
- Wind power forecasting
- Wind speed
- Work (physics)
- Feature (linguistics)
- Power (physics)
- Affordable and clean energy