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Power Prediction of Regional Photovoltaic Power Stations Based on Meteorological Encryption and Spatio-Temporal Graph Networks

Author

Listed:
  • Shunli Deng

    (School of Electrical Engineering, Xinjiang University, Urumqi 830047, China)

  • Shuangxi Cui

    (School of Electrical Engineering, Xinjiang University, Urumqi 830047, China)

  • Anchen Xu

    (School of Electrical Engineering, Xinjiang University, Urumqi 830047, China)

Abstract

Distributed photovoltaic (PV) power stations generally lack historical meteorological data, which is one of the main reasons for their insufficient power prediction accuracy. To address this issue, this paper proposes a power prediction method for regional distributed PV power stations based on meteorological encryption and spatio-temporal graph networks. First, inverse distance weighted meteorological encryption technology is used to achieve the comprehensive coverage of key meteorological resources based on the geographical locations of PV power stations and the meteorological resources of weather stations. Next, the historical power correlations between PV power stations are analyzed, and highly correlated stations are connected to construct a topological graph structure. Then, an improved spatio-temporal graph network model is established based on this graph to deeply mine the spatio-temporal characteristics of regional PV power stations. Furthermore, a dual-layer attention mechanism is added to further learn the feature attributes of nodes and enhance the spatio-temporal features extracted by the spatio-temporal graph network, ultimately achieving power prediction for regional PV power stations. The simulation results indicate that the proposed model demonstrates excellent prediction accuracy, robustness, extensive generalization capability, and broad applicability.

Suggested Citation

  • Shunli Deng & Shuangxi Cui & Anchen Xu, 2024. "Power Prediction of Regional Photovoltaic Power Stations Based on Meteorological Encryption and Spatio-Temporal Graph Networks," Energies, MDPI, vol. 17(14), pages 1-22, July.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:14:p:3557-:d:1438786
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    References listed on IDEAS

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    1. Limouni, Tariq & Yaagoubi, Reda & Bouziane, Khalid & Guissi, Khalid & Baali, El Houssain, 2023. "Accurate one step and multistep forecasting of very short-term PV power using LSTM-TCN model," Renewable Energy, Elsevier, vol. 205(C), pages 1010-1024.
    2. Huang, Xiaoqiao & Li, Qiong & Tai, Yonghang & Chen, Zaiqing & Liu, Jun & Shi, Junsheng & Liu, Wuming, 2022. "Time series forecasting for hourly photovoltaic power using conditional generative adversarial network and Bi-LSTM," Energy, Elsevier, vol. 246(C).
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    Cited by:

    1. Yubo Wang & Chao Huo & Fei Xu & Libin Zheng & Ling Hao, 2025. "Ultra-Short-Term Distributed Photovoltaic Power Probabilistic Forecasting Method Based on Federated Learning and Joint Probability Distribution Modeling," Energies, MDPI, vol. 18(1), pages 1-21, January.

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