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Wind Speed Prediction for Offshore Sites Using a Clockwork Recurrent Network

Author

Listed:
  • Yuxuan Shi

    (School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China)

  • Yanyu Wang

    (School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China)

  • Haoran Zheng

    (School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China)

Abstract

Offshore sites show greater potential for wind energy utilization than most onshore sites. When planning an offshore wind power farm, the speed of offshore wind is used to estimate various operation parameters, such as the power output, extreme wind load, and fatigue load. Accurate speed prediction is crucial to the running of wind power farms and the security of smart grids. Unlike onshore wind, offshore wind has the characteristics of random, intermittent, and chaotic, which will cause the time series of wind speeds to have strong nonlinearity. It will bring greater difficulties to offshore wind speed predictions, which traditional recurrent neural networks cannot deal with for lacking in long-term dependency. An offshore wind speed prediction method is proposed by using a clockwork recurrent network (CWRNN). In a CWRNN model, the hidden layer is subdivided into several parts and each part is allocated a different clock speed. Under the mechanism, the long-term dependency of the recurrent neural network can be easily addressed, which can furthermore effectively solve the problem of strong nonlinearity in offshore speed winds. The experiments are performed by using the actual data of two different offshore sites located in the Caribbean Sea and one onshore site located in the interior of the United States, to verify the performance of the model. The results show that the prediction model achieves significant accuracy improvement.

Suggested Citation

  • Yuxuan Shi & Yanyu Wang & Haoran Zheng, 2022. "Wind Speed Prediction for Offshore Sites Using a Clockwork Recurrent Network," Energies, MDPI, vol. 15(3), pages 1-18, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:751-:d:729445
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    References listed on IDEAS

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