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The data-based adaptive graph learning network for analysis and prediction of offshore wind speed

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  • Ren, Yuting
  • Li, Zhuolin
  • Xu, Lingyu
  • Yu, Jie

Abstract

Offshore wind power plays an important role in the economy because of its abundant resources and great potential. Therefore, predicting offshore wind power significantly affects the intelligent management of power generation. However, tackling such forecasting task usually meet huge challenges due to the complex-temporal dependence on offshore wind data. Recently, deep learning approaches have successfully demonstrated their ability in modeling time series data. However, they often have significant limitations for failing to explore dynamic spatio-temporal dependencies between signals. In this paper, we propose a new framework DAGLN, which performs spatial dependency modelling through data-driven graph construction and graph learning, breaking through the limitations of predefined graph structures to obtain high-dimensional spatial features and capturing temporal information from them based on GRU structure. The model can play a powerful role in mining spatio-temporal correlations in multi-node and multi-step wind speed data prediction. Extensive experiments on selected nodes and data in the China Sea show the developed approach can outperform state-of-art models in multi-node wind speed prediction.

Suggested Citation

  • Ren, Yuting & Li, Zhuolin & Xu, Lingyu & Yu, Jie, 2023. "The data-based adaptive graph learning network for analysis and prediction of offshore wind speed," Energy, Elsevier, vol. 267(C).
    • Handle: RePEc:eee:energy:v:267:y:2023:i:c:s0360544222034776
    DOI: 10.1016/j.energy.2022.126590
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    References listed on IDEAS

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    1. Yang, Mao & Han, Chao & Zhang, Wei & Wang, Bo, 2024. "A short-term power prediction method for wind farm cluster based on the fusion of multi-source spatiotemporal feature information," Energy, Elsevier, vol. 294(C).

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