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A centralized power prediction method for large-scale wind power clusters based on dynamic graph neural network

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  • Yang, Mao
  • Wang, Da
  • Zhang, Wei
  • Yv, Xinnan

Abstract

The uncertainty of the wind process leads to the randomness of its regional propagation, and the spatial correlation between nearby wind farms also shows a dynamic change tendency under the influence of wind direction. To consider the influence of dynamic spatial correlation on wind power prediction modeling, a short-term wind power prediction method based on a dynamic graph neural network is proposed. First, the topology graph of the wind farm cluster was established to represent the correlation of wind farms based on graph theory. Then, a dynamic spatiotemporal graph neural network was constructed to adapt graph topology by node embedding, which can explore the changing characteristics of spatial correlation among wind farms. Finally, we proposed a decoupling error model that can quantify the proportion of errors caused by the modeling process, which can assist in evaluating the performance of predictive models. We conducted experiments using data provided by the wind farm cluster in Inner Mongolia, and the average normalized Root Mean Square Error for the 12 wind farms was 0.1424, which verified the effectiveness of the proposed model.

Suggested Citation

  • Yang, Mao & Wang, Da & Zhang, Wei & Yv, Xinnan, 2024. "A centralized power prediction method for large-scale wind power clusters based on dynamic graph neural network," Energy, Elsevier, vol. 310(C).
    • Handle: RePEc:eee:energy:v:310:y:2024:i:c:s0360544224029852
    DOI: 10.1016/j.energy.2024.133210
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    References listed on IDEAS

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    1. Yan Chen & Miaolin Yu & Haochong Wei & Huanxing Qi & Yiming Qin & Xiaochun Hu & Rongxing Jiang, 2025. "A Lightweight Framework for Rapid Response to Short-Term Forecasting of Wind Farms Using Dual Scale Modeling and Normalized Feature Learning," Energies, MDPI, vol. 18(3), pages 1-20, January.

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