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An online transfer learning model for wind turbine power prediction based on spatial feature construction and system-wide update

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  • Liu, Ling
  • Wang, Jujie
  • Li, Jianping
  • Wei, Lu

Abstract

Accurate prediction of wind turbine power is important for the safe operation of wind farms. However, most of the previous online transfer learning methods are partially updated and time-consuming. Here we propose a novel system-wide update online transfer learning model to overcome these shortcomings. To improve the multi-source data fusion accuracy, a new time trend quantification method is applied to expand the data source, a convolutional neural network multi-source data fusion method is proposed to reduce the dimension of data, and a Hilbert spatial feature construction method is used to construct spatial information of data. To achieve system-wide update and rapid prediction, we have deleted the weight unit of traditional method and added two data buffers. The results show that: (1) the proposed multi-source data processing method has the smallest mapping errors, which mean absolute error for all wind turbines is less than 32.1; (2) the proposed online transfer learning model has the highest prediction accuracy, which is higher than 92.5%.

Suggested Citation

  • Liu, Ling & Wang, Jujie & Li, Jianping & Wei, Lu, 2023. "An online transfer learning model for wind turbine power prediction based on spatial feature construction and system-wide update," Applied Energy, Elsevier, vol. 340(C).
    • Handle: RePEc:eee:appene:v:340:y:2023:i:c:s0306261923004130
    DOI: 10.1016/j.apenergy.2023.121049
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    3. Wen-Chang Tsai & Chih-Ming Hong & Chia-Sheng Tu & Whei-Min Lin & Chiung-Hsing Chen, 2023. "A Review of Modern Wind Power Generation Forecasting Technologies," Sustainability, MDPI, vol. 15(14), pages 1-40, July.

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