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Deep Learning for Wave Energy Converter Modeling Using Long Short-Term Memory

Author

Listed:
  • Seyed Milad Mousavi

    (Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 1999143344, Iran)

  • Majid Ghasemi

    (Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 1999143344, Iran)

  • Mahsa Dehghan Manshadi

    (Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 1999143344, Iran)

  • Amir Mosavi

    (Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany
    John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary
    School of the Built Environment, Oxford Brookes University, Oxford OX3 0BP, UK)

Abstract

Accurate forecasts of ocean waves energy can not only reduce costs for investment, but it is also essential for the management and operation of electrical power. This paper presents an innovative approach based on long short-term memory (LSTM) to predict the power generation of an economical wave energy converter named “Searaser”. The data for analysis is provided by collecting the experimental data from another study and the exerted data from a numerical simulation of Searaser. The simulation is performed with Flow-3D software, which has high capability in analyzing fluid–solid interactions. The lack of relation between wind speed and output power in previous studies needs to be investigated in this field. Therefore, in this study, wind speed and output power are related with an LSTM method. Moreover, it can be inferred that the LSTM network is able to predict power in terms of height more accurately and faster than the numerical solution in a field of predicting. The network output figures show a great agreement, and the root mean square is 0.49 in the mean value related to the accuracy of the LSTM method. Furthermore, the mathematical relation between the generated power and wave height was introduced by curve fitting of the power function to the result of the LSTM method.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:8:p:871-:d:536566
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    References listed on IDEAS

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    Cited by:

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    2. Panyu Tang & Mahdi Aghaabbasi & Mujahid Ali & Amin Jan & Abdeliazim Mustafa Mohamed & Abdullah Mohamed, 2022. "How Sustainable Is People’s Travel to Reach Public Transit Stations to Go to Work? A Machine Learning Approach to Reveal Complex Relationships," Sustainability, MDPI, vol. 14(7), pages 1-18, March.
    3. Fatemehsadat Mirshafiee & Emad Shahbazi & Mohadeseh Safi & Rituraj Rituraj, 2023. "Predicting Power and Hydrogen Generation of a Renewable Energy Converter Utilizing Data-Driven Methods: A Sustainable Smart Grid Case Study," Energies, MDPI, vol. 16(1), pages 1-20, January.
    4. Jialin Liu & Chen Gong & Suhua Chen & Nanrun Zhou, 2023. "Multi-Step-Ahead Wind Speed Forecast Method Based on Outlier Correction, Optimized Decomposition, and DLinear Model," Mathematics, MDPI, vol. 11(12), pages 1-26, June.

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