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Probabilistic wind power forecasting for newly-built wind farms based on multi-task Gaussian process method

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  • Liao, Qishu
  • Cao, Di
  • Chen, Zhe
  • Blaabjerg, Frede
  • Hu, Weihao

Abstract

The accurate training of a wind power forecasting (WPF) model for a newly built wind farm is difficult because of limited historical data. This study established a multitask learning architecture wherein the WPF in different wind farms represents an independent task. Subsequently, a novel short-term WPF model based on a multitask learning architecture was proposed. In this model, a multitask Gaussian process is used to capture the intertask conjunction, which contributed to the training of each task. The proposed methodological framework employs dependencies from other tasks wherein older wind farms contain substantial historical data to enhance the performance of tasks in which there is a newly built wind farm. Several numerical experiments were conducted using datasets from seven independent wind farms in Australia. The results show that the proposed scheme not only obtains improved point forecasting results but also produces better probabilistic forecasting results, thus demonstrating the superiority of the proposed method.

Suggested Citation

  • Liao, Qishu & Cao, Di & Chen, Zhe & Blaabjerg, Frede & Hu, Weihao, 2023. "Probabilistic wind power forecasting for newly-built wind farms based on multi-task Gaussian process method," Renewable Energy, Elsevier, vol. 217(C).
    • Handle: RePEc:eee:renene:v:217:y:2023:i:c:s0960148123009680
    DOI: 10.1016/j.renene.2023.119054
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    References listed on IDEAS

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    1. Landry, Mark & Erlinger, Thomas P. & Patschke, David & Varrichio, Craig, 2016. "Probabilistic gradient boosting machines for GEFCom2014 wind forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1061-1066.
    2. Hong, Ying-Yi & Rioflorido, Christian Lian Paulo P., 2019. "A hybrid deep learning-based neural network for 24-h ahead wind power forecasting," Applied Energy, Elsevier, vol. 250(C), pages 530-539.
    3. Chen, Kuilin & Yu, Jie, 2014. "Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach," Applied Energy, Elsevier, vol. 113(C), pages 690-705.
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    Cited by:

    1. Li, Jianfang & Jia, Li & Zhou, Chengyu, 2024. "Probability density function based adaptive ensemble learning with global convergence for wind power prediction," Energy, Elsevier, vol. 312(C).

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