Journal of Shandong University(Engineering Science) ›› 2026, Vol. 56 ›› Issue (2): 130-138.doi: 10.6040/j.issn.1672-3961.0.2025.030

• Electrical Engineering • Previous Articles    

Short-term wind power prediction model based on spatial-temporal graph convolutional network with dual-graph structure

ZHENG Zheming1,2, KONG Lingling1,2*, HE Yin1,2   

  1. ZHENG Zheming1, 2, KONG Lingling1, 2*, HE Yin1, 2(1. School of Electrical and Information Engineering, Yunnan Minzu University, Kunming 650504, Yunnan, China;
    2. Yunnan Province Key Laboratory of Unmanned Autonomous Systems(Yunnan Minzu University), Kunming 650504, Yunnan, China
  • Published:2026-04-13

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Abstract: To address the limitations of traditional wind power prediction methods that ignored the interaction of spatial-temporal features, a spatial-temporal graph convolutional network with attention(STGCN-A)was proposed. A correlation matrix was constructed by the maximal information coefficient to form a statistical correlation-based spatial graph, while an Euclidean distance-based geographic proximity spatial graph was built to achieve dual-graph modeling among wind turbines. Spatial features were extracted through a graph convolutional network(GCN), and temporal dependencies were captured by a gated recurrent unit(GRU). An attention mechanism(AM)was introduced to dynamically weight different time steps, enhancing the representation of critical information in spatial-temporal features. Comparative experiments on real wind power datasets demonstrated that the proposed model outperformed traditional methods in terms of root mean square error(ERMS), mean absolute error(EMA), and coefficient of determination(R2). The results indicated higher prediction accuracy and strong potential for practical applications.

Key words: wind power, graph convolutional network, gated recurrent unit, attention mechanism, maximal information coefficient

CLC Number: 

  • TM614
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