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A graph attention network with spatio-temporal wind propagation graph for wind power ramp events prediction

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  • Peng, Xinghao
  • Li, Yanting
  • Tsung, Fugee

Abstract

The increasing penetration rate of wind power underscores the necessity for accurate forecasting and alerting of wind power ramp events (WPREs), given the unpredictable nature of wind. This article presents a novel approach to predicting WPREs, emphasizing the complexities of wind propagation across multiple locations, in contrast to traditional single-site analyses. By integrating a wind propagation graph into a Graph Attention Network (GAT), the prediction of ramp events is significantly enhanced. The efficacy of this approach is validated through comprehensive case studies utilizing the Spatial Dynamic Wind Power Forecasting (SDWPF) dataset from the Baidu KDD Cup 2022 and the WIND toolkit dataset from NREL, demonstrating superior results at both the wind turbine and wind farm scales.

Suggested Citation

  • Peng, Xinghao & Li, Yanting & Tsung, Fugee, 2024. "A graph attention network with spatio-temporal wind propagation graph for wind power ramp events prediction," Renewable Energy, Elsevier, vol. 236(C).
    • Handle: RePEc:eee:renene:v:236:y:2024:i:c:s096014812401348x
    DOI: 10.1016/j.renene.2024.121280
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    References listed on IDEAS

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    1. Jie Zhang & Xinchun Zhu & Yigong Xie & Guo Chen & Shuangquan Liu, 2025. "Detection and Prediction of Wind and Solar Photovoltaic Power Ramp Events Based on Data-Driven Methods: A Critical Review," Energies, MDPI, vol. 18(13), pages 1-20, June.
    2. Yang, Yakai & Fan, Shuanglong & Liu, Zhenqing & Yu, Zhongze, 2025. "WD-SGformer: high-precision wind power forecasting via dual-attention dynamic spatio-temporal learning," Energy, Elsevier, vol. 337(C).
    3. Yakai Yang & Zhenqing Liu & Zhongze Yu, 2025. "SA-STGCN: A Spectral-Attentive Spatio-Temporal Graph Convolutional Network for Wind Power Forecasting with Wavelet-Enhanced Multi-Scale Learning," Energies, MDPI, vol. 18(19), pages 1-20, October.
    4. Yingrui Chen & Jiarong Shi, 2025. "Broad Random Forest: A Lightweight Prediction Model for Short-Term Wind Power by Fusing Broad Learning and Random Forest," Sustainability, MDPI, vol. 17(11), pages 1-16, May.

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