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Comparative Investigations of Tidal Current Velocity Prediction Considering Effect of Multi-Layer Current Velocity

Author

Listed:
  • Bo Feng

    (Ocean College, Zhejiang University, Hangzhou 310000, China)

  • Peng Qian

    (Ocean College, Zhejiang University, Hangzhou 310000, China)

  • Yulin Si

    (Ocean College, Zhejiang University, Hangzhou 310000, China)

  • Xiaodong Liu

    (Ocean College, Zhejiang University, Hangzhou 310000, China)

  • Haixiao Yang

    (Ocean College, Zhejiang University, Hangzhou 310000, China)

  • Huisheng Wen

    (Ocean College, Zhejiang University, Hangzhou 310000, China)

  • Dahai Zhang

    (Ocean College, Zhejiang University, Hangzhou 310000, China)

Abstract

Accurate tidal current prediction plays a critical role with increasing utilization of tidal energy. The classical prediction approach of the tidal current velocity adopts the harmonic analysis (HA) method. The performance of the HA approach is not ideal to predict the high frequency components of tidal currents due to the lack of capability processing the non-astronomic factor. Recently, machine learning algorithms have been applied to process the non-astronomic factor in the prediction of tidal current. In this paper, a tidal current velocity prediction considering the effect of the multi-layer current velocity method is proposed. The proposed method adopts three machine learning algorithms to establish the prediction models for comparative investigations, namely long-short term memory (LSTM), back-propagation (BP) neural network, and the Elman regression network. In the case study, the tidal current data collected from the real ocean environment were used to validate the proposed method. The results show that the proposed method combined with the LSTM algorithm had higher accuracy than both the commercial tidal prediction tool (UTide) and the other two algorithms. This paper presents a novel tidal current velocity prediction considering the effect of the multi-layer current velocity method, which improves the accuracy of the power flow prediction and contributes to the research in the field of tidal current velocity prediction and the capture of tidal energy.

Suggested Citation

  • Bo Feng & Peng Qian & Yulin Si & Xiaodong Liu & Haixiao Yang & Huisheng Wen & Dahai Zhang, 2020. "Comparative Investigations of Tidal Current Velocity Prediction Considering Effect of Multi-Layer Current Velocity," Energies, MDPI, vol. 13(23), pages 1-19, December.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:23:p:6417-:d:456992
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    References listed on IDEAS

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    1. Vazquez, A. & Iglesias, G., 2015. "LCOE (levelised cost of energy) mapping: A new geospatial tool for tidal stream energy," Energy, Elsevier, vol. 91(C), pages 192-201.
    2. Qian, Peng & Feng, Bo & Liu, Hao & Tian, Xiange & Si, Yulin & Zhang, Dahai, 2019. "Review on configuration and control methods of tidal current turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 125-139.
    3. Wang, Shujie & Yuan, Peng & Li, Dong & Jiao, Yuhe, 2011. "An overview of ocean renewable energy in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 91-111, January.
    4. Uihlein, Andreas & Magagna, Davide, 2016. "Wave and tidal current energy – A review of the current state of research beyond technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1070-1081.
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    Cited by:

    1. Liu, Xiaodong & Chen, Zheng & Si, Yulin & Qian, Peng & Wu, He & Cui, Lin & Zhang, Dahai, 2021. "A review of tidal current energy resource assessment in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).

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