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Dynamic spatio-temporal correlation and hierarchical directed graph structure based ultra-short-term wind farm cluster power forecasting method

Author

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  • Wang, Fei
  • Chen, Peng
  • Zhen, Zhao
  • Yin, Rui
  • Cao, Chunmei
  • Zhang, Yagang
  • Duić, Neven

Abstract

Accurate wind farm cluster power forecasting is of great significance for the safe operation of the power system with high wind power penetration. However, most of the current neural network methods used for wind farm cluster power forecasting have the following three problems: (1) lack of consideration of dynamic spatio-temporal correlation among adjacent wind farms; (2) simultaneously forecasting all wind farms’ power to obtain the total power will produce numerous error sources; (3) ignoring the causal relationship among input variables. Therefore, to solve the above problems, this paper proposes an ultra-short-term wind farm cluster power forecasting method based on dynamic spatio-temporal correlation and hierarchical directed graph structure. Firstly, three different types of nodes (wind speed nodes, wind power nodes, and target node) and input samples are defined, and then the spatio-temporal correlation matrices that can describe the correlation of adjacent wind farms are also calculated. Secondly, directed edges are defined to connect different nodes in order to obtain the hierarchical directed graph structure. Finally, this graph structure with dynamic spatio-temporal correlation information is used to train the forecasting model. In case study, compared with other benchmark methods, the proposed method shows excellent performance in improving accuracy of power forecasting.

Suggested Citation

  • Wang, Fei & Chen, Peng & Zhen, Zhao & Yin, Rui & Cao, Chunmei & Zhang, Yagang & Duić, Neven, 2022. "Dynamic spatio-temporal correlation and hierarchical directed graph structure based ultra-short-term wind farm cluster power forecasting method," Applied Energy, Elsevier, vol. 323(C).
    • Handle: RePEc:eee:appene:v:323:y:2022:i:c:s0306261922008893
    DOI: 10.1016/j.apenergy.2022.119579
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    References listed on IDEAS

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    Cited by:

    1. Bentsen, Lars Ødegaard & Warakagoda, Narada Dilp & Stenbro, Roy & Engelstad, Paal, 2023. "Spatio-temporal wind speed forecasting using graph networks and novel Transformer architectures," Applied Energy, Elsevier, vol. 333(C).
    2. Liu, Jiarui & Fu, Yuchen, 2023. "Renewable energy forecasting: A self-supervised learning-based transformer variant," Energy, Elsevier, vol. 284(C).
    3. Xu, Xuefang & Hu, Shiting & Shao, Huaishuang & Shi, Peiming & Li, Ruixiong & Li, Deguang, 2023. "A spatio-temporal forecasting model using optimally weighted graph convolutional network and gated recurrent unit for wind speed of different sites distributed in an offshore wind farm," Energy, Elsevier, vol. 284(C).
    4. He Yin & Hai Lan & Ying-Yi Hong & Zhuangwei Wang & Peng Cheng & Dan Li & Dong Guo, 2023. "A Comprehensive Review of Shipboard Power Systems with New Energy Sources," Energies, MDPI, vol. 16(5), pages 1-44, February.
    5. Xin Zhao & Qiushuang Li & Wanlei Xue & Yihang Zhao & Huiru Zhao & Sen Guo, 2022. "Research on Ultra-Short-Term Load Forecasting Based on Real-Time Electricity Price and Window-Based XGBoost Model," Energies, MDPI, vol. 15(19), pages 1-11, October.
    6. Liu, Jiarui & Fu, Yuchen, 2023. "Decomposition spectral graph convolutional network based on multi-channel adaptive adjacency matrix for renewable energy prediction," Energy, Elsevier, vol. 284(C).
    7. Fan, Huijing & Zhen, Zhao & Liu, Nian & Sun, Yiqian & Chang, Xiqiang & Li, Yu & Wang, Fei & Mi, Zengqiang, 2023. "Fluctuation pattern recognition based ultra-short-term wind power probabilistic forecasting method," Energy, Elsevier, vol. 266(C).

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