Spatiotemporal wind energy forecasting: a comprehensive survey and a deep equilibrium-based case study with stemGNN

Aquino, Luiza Scapinello; Seman, Laio Oriel; Mariani, Viviana Cocco; Coelho, Leandro Dos Santos; Stefenon, Stefano Frizzo; González, Gabriel Villarrubia

http://hdl.handle.net/10400.21/22286

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Authors

Aquino, Luiza Scapinello

Seman, Laio Oriel

Mariani, Viviana Cocco

Coelho, Leandro Dos Santos

Stefenon, Stefano Frizzo

González, Gabriel Villarrubia

Abstract(s)

Accurate spatiotemporal wind energy forecasting is essential for ensuring grid stability and maximizing the efficiency of renewable energy systems. This paper addresses the challenge of modeling the complex spatial and temporal dependencies inherent in wind power generation by presenting a comprehensive survey of existing spatiotemporal forecasting methods and introducing an innovative deep learning approach. The proposed model integrates a Graph Neural Network (GNN) to represent wind turbines as nodes within a graph, capturing spatial relationships, while a Deep Equilibrium Model (DEQ) enables equilibrium-based inference to handle highly nonlinear wind patterns. A Sequence-to-Sequence (Seq2Seq) architecture further manages temporal dependencies. The method was validated using a real-world dataset of wind power generation, outperforming baseline models across multiple forecast horizons and maintaining stable accuracy across short- and mid-term predictions. Results demonstrate that the proposed GNN with DEQ effectively models both spatial and temporal dynamics for Seq2Seq data, improving prediction accuracy while maintaining computational efficiency. This study highlights the potential of equilibrium-based spatiotemporal graph models for wind energy forecasting and provides a robust tool for better integration of wind power into modern power grids.

Keywords

Wind energy Graph neural network Deep equilibrium Spatiotemporal forecasting

URI

http://hdl.handle.net/10400.21/22286

Citation

Aquino, L. S., Seman, L. O., Mariani, V. C., Coelho, L. S., Stefenon, S. F., & González, G. V. (2025). Spatiotemporal wind energy forecasting: a comprehensive survey and a deep equilibrium-based case study with stemGNN. IEEE Access, 13, 131461- 131482. https://doi.org/10.1109/ACCESS.2025.3586997