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Review on Deep Learning Research and Applications in Wind and Wave Energy

Author

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  • Chengcheng Gu

    (Mechanical and Industrial Engineering Department, Texas A&M University-Kingsville, Kingsville, TX 78363, USA)

  • Hua Li

    (Mechanical and Industrial Engineering Department, Texas A&M University-Kingsville, Kingsville, TX 78363, USA)

Abstract

Wind energy and wave energy are considered to have enormous potential as renewable energy sources in the energy system to make great contributions in transitioning from fossil fuel to renewable energy. However, the uncertain, erratic, and complicated scenarios, as well as the tremendous amount of information and corresponding parameters, associated with wind and wave energy harvesting are difficult to handle. In the field of big data handing and mining, artificial intelligence plays a critical and efficient role in energy system transition, harvesting and related applications. The derivative method of deep learning and its surrounding prolongation structures are expanding more maturely in many fields of applications in the last decade. Even though both wind and wave energy have the characteristics of instability, more and more applications have implemented using these two renewable energy sources with the support of deep learning methods. This paper systematically reviews and summarizes the different models, methods and applications where the deep learning method has been applied in wind and wave energy. The accuracy and effectiveness of different methods on a similar application were compared. This paper concludes that applications supported by deep learning have enormous potential in terms of energy optimization, harvesting, management, forecasting, behavior exploration and identification.

Suggested Citation

  • Chengcheng Gu & Hua Li, 2022. "Review on Deep Learning Research and Applications in Wind and Wave Energy," Energies, MDPI, vol. 15(4), pages 1-19, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1510-:d:752131
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    3. Jamei, Mehdi & Ali, Mumtaz & Karbasi, Masoud & Xiang, Yong & Ahmadianfar, Iman & Yaseen, Zaher Mundher, 2022. "Designing a Multi-Stage Expert System for daily ocean wave energy forecasting: A multivariate data decomposition-based approach," Applied Energy, Elsevier, vol. 326(C).
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    6. Pasta, Edoardo & Faedo, Nicolás & Mattiazzo, Giuliana & Ringwood, John V., 2023. "Towards data-driven and data-based control of wave energy systems: Classification, overview, and critical assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    7. Francisco Haces-Fernandez & Hua Li & David Ramirez, 2022. "Analysis of Wave Energy Behavior and Its Underlying Reasons in the Gulf of Mexico Based on Computer Animation and Energy Events Concept," Sustainability, MDPI, vol. 14(8), pages 1-23, April.
    8. Youjun Sun & Huajun Zhang & Shulin Hu & Jun Shi & Jianning Geng & Yixin Su, 2023. "ConvGRU-RMWP: A Regional Multi-Step Model for Wave Height Prediction," Mathematics, MDPI, vol. 11(9), pages 1-21, April.
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