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Prediction of Wave Power Generation Using a Convolutional Neural Network with Multiple Inputs

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  • Chenhua Ni

    (National Ocean Technology Center, Tianjin 300112, China
    Engineering Department, Lancaster University, Bailrigg, Lancaster LA1 4YW, UK)

  • Xiandong Ma

    (Engineering Department, Lancaster University, Bailrigg, Lancaster LA1 4YW, UK)

Abstract

Successful development of a marine wave energy converter (WEC) relies strongly on the development of the power generation device, which needs to be efficient and cost-effective. An innovative multi-input approach based on the Convolutional Neural Network (CNN) is investigated to predict the power generation of a WEC system using a double-buoy oscillating body device (OBD). The results from the experimental data show that the proposed multi-input CNN performs much better at predicting results compared with the conventional artificial network and regression models. Through the power generation analysis of this double-buoy OBD, it shows that the power output has a positive correlation with the wave height when it is higher than 0.2 m, which becomes even stronger if the wave height is higher than 0.6 m. Furthermore, the proposed approach associated with the CNN algorithm in this study can potentially detect the changes that could be due to presence of anomalies and therefore be used for condition monitoring and fault diagnosis of marine energy converters. The results are also able to facilitate controlling of the electricity balance among energy conversion, wave power produced and storage.

Suggested Citation

  • Chenhua Ni & Xiandong Ma, 2018. "Prediction of Wave Power Generation Using a Convolutional Neural Network with Multiple Inputs," Energies, MDPI, vol. 11(8), pages 1-18, August.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:8:p:2097-:d:163402
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    References listed on IDEAS

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    Cited by:

    1. Seyed Milad Mousavi & Majid Ghasemi & Mahsa Dehghan Manshadi & Amir Mosavi, 2021. "Deep Learning for Wave Energy Converter Modeling Using Long Short-Term Memory," Mathematics, MDPI, vol. 9(8), pages 1-16, April.
    2. Simon Thomas & Mikael Eriksson & Malin Göteman & Martyn Hann & Jan Isberg & Jens Engström, 2018. "Experimental and Numerical Collaborative Latching Control of Wave Energy Converter Arrays," Energies, MDPI, vol. 11(11), pages 1-16, November.
    3. Dongwoo Seo & Taesang Huh & Myungil Kim & Jaesoon Hwang & Daeyong Jung, 2021. "Prediction of Air Pressure Change Inside the Chamber of an Oscillating Water Column–Wave Energy Converter Using Machine-Learning in Big Data Platform," Energies, MDPI, vol. 14(11), pages 1-17, May.
    4. Yang, Shaobo & Deng, Zegui & Li, Xingfei & Zheng, Chongwei & Xi, Lintong & Zhuang, Jucheng & Zhang, Zhenquan & Zhang, Zhiyou, 2021. "A novel hybrid model based on STL decomposition and one-dimensional convolutional neural networks with positional encoding for significant wave height forecast," Renewable Energy, Elsevier, vol. 173(C), pages 531-543.

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