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Graph Neural Networks in Wind Power Forecasting

arXiv 2507.00105 ↗First posted 30 Jun 2025cs.LG cs.SY eess.SYcited by 0

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Abstract (arXiv)

We study the applicability of GNNs to the problem of wind energy forecasting. We find that certain architectures achieve performance comparable to our best CNN-based benchmark. The study is conducted on three wind power facilities using five years of historical data. Numerical Weather Prediction (NWP) variables were used as predictors, and models were evaluated on a 24 to 36 hour ahead test horizon.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 10 references that are in the map. First posted on arXiv 30 Jun 2025.

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References · 5

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2020Learning to Simulate Complex Physics with Graph Networkscited by 880
  4. 2021How Attentive are Graph Attention Networks?cited by 835
  5. 2017A Generalization of Convolutional Neural Networks to Graph-Structured Datacited by 9

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