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Distribution Network Situational Awareness Prediction Based on Spatio-Temporal Attention Dynamic Graph Neural Network

Author

Listed:
  • Xixi Qiu

    (School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 401331, China)

  • Yuteng Huang

    (State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 311000, China)

  • Guojin Liu

    (School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 401331, China)

  • Jiaxiang Yan

    (State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 311000, China)

  • Shan Chen

    (State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 311000, China)

Abstract

Distribution network situational awareness prediction is a key technology for ensuring the safe and stable operation of distribution networks. However, most existing methods suffer from spatio-temporal dynamic correlation and dynamic topology, resulting in unsatisfactory performance. To address these issues, we propose a distribution network situational awareness prediction method based on a spatio-temporal attention dynamic graph neural network model that realizes the decoupling of spatio-temporal features of the distribution network data by adopting the alternating stacking of the multi-head self-attention mechanism with temporal dynamic perception and the spatial dynamic graph convolution module. Furthermore, the dynamic correlation matrix is introduced to adaptively adjust the node interaction weights to effectively handle the network dynamic topology information. Through extensive experiments, the proposed method outperforms eight baseline models.

Suggested Citation

  • Xixi Qiu & Yuteng Huang & Guojin Liu & Jiaxiang Yan & Shan Chen, 2025. "Distribution Network Situational Awareness Prediction Based on Spatio-Temporal Attention Dynamic Graph Neural Network," Energies, MDPI, vol. 18(16), pages 1-20, August.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:16:p:4402-:d:1727059
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    References listed on IDEAS

    as
    1. Jiang, He & Dong, Yawei & Dong, Yao & Wang, Jianzhou, 2024. "Power load forecasting based on spatial–temporal fusion graph convolution network," Technological Forecasting and Social Change, Elsevier, vol. 204(C).
    2. Simeunović, Jelena & Schubnel, Baptiste & Alet, Pierre-Jean & Carrillo, Rafael E. & Frossard, Pascal, 2022. "Interpretable temporal-spatial graph attention network for multi-site PV power forecasting," Applied Energy, Elsevier, vol. 327(C).
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