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Interpretable Wind Power Short-Term Power Prediction Model Using Deep Graph Attention Network

Author

Listed:
  • Jinhua Zhang

    (School of Electrical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China)

  • Hui Li

    (School of Electrical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China)

  • Peng Cheng

    (School of Mathematics and Statistics, North China University of Water Resources and Electric Power, Zhengzhou 450045, China)

  • Jie Yan

    (School of New Energy, North China Electric Power University, Beijing 100096, China)

Abstract

High-precision spatial-temporal wind power prediction technology is of great significance for ensuring the safe and stable operation of power grids. The development of artificial intelligence technology provides a new scheme for modeling with strong spatial-temporal correlation. In addition, the existing prediction models are mostly ‘black box’ models, lacking interpretability, which may lead to a lack of trust in the model by power grid dispatchers. Therefore, improving the model to obtain interpretability has become an important challenge. In this paper, an interpretable short-term wind power prediction model based on ensemble deep graph neural network is designed. Firstly, the graph network model (GNN) with an attention mechanism is applied to the aggregate and the spatial-temporal features of wind power data are extracted, and the interpretable ability is obtained. Then, the long short-term memory (LSTM) method is used to process the extracted features and establish a wind power prediction model. Finally, the random sampling algorithm is used to optimize the hyperparameters to improve the learning rate and performance of the model. Through multiple comparative experiments and a case analysis, the results show that the proposed model has a higher prediction accuracy than other traditional models and obtains reasonable interpretability in time and space dimensions.

Suggested Citation

  • Jinhua Zhang & Hui Li & Peng Cheng & Jie Yan, 2024. "Interpretable Wind Power Short-Term Power Prediction Model Using Deep Graph Attention Network," Energies, MDPI, vol. 17(2), pages 1-16, January.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:2:p:384-:d:1317922
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. 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).

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