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Predicting Power and Hydrogen Generation of a Renewable Energy Converter Utilizing Data-Driven Methods: A Sustainable Smart Grid Case Study

Author

Listed:
  • Fatemehsadat Mirshafiee

    (Department of Electrical and Computer Engineering, K.N. Toosi University of Technology, Tehran 1999143344, Iran)

  • Emad Shahbazi

    (Department of Mechatronic, Amirkabir University of Technology, Tehran 158754413, Iran)

  • Mohadeseh Safi

    (Department of Mechatronic, Electrical and Computer Engineering, University of Tehran, Tehran 1416634793, Iran)

  • Rituraj Rituraj

    (Doctoral School of Applied Informatics and Applied Mathematics, Faculty of Informatics, Obuda University, 1023 Budapest, Hungary)

Abstract

This study proposes a data-driven methodology for modeling power and hydrogen generation of a sustainable energy converter. The wave and hydrogen production at different wave heights and wind speeds are predicted. Furthermore, this research emphasizes and encourages the possibility of extracting hydrogen from ocean waves. By using the extracted data from the FLOW-3D software simulation and the experimental data from the special test in the ocean, the comparison analysis of two data-driven learning methods is conducted. The results show that the amount of hydrogen production is proportional to the amount of generated electrical power. The reliability of the proposed renewable energy converter is further discussed as a sustainable smart grid application.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:1:p:502-:d:1022849
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    Cited by:

    1. Jimmy Gallegos & Paul Arévalo & Christian Montaleza & Francisco Jurado, 2024. "Sustainable Electrification—Advances and Challenges in Electrical-Distribution Networks: A Review," Sustainability, MDPI, vol. 16(2), pages 1-33, January.
    2. Hafize Nurgul Durmus Senyapar & Ramazan Bayindir, 2023. "The Research Agenda on Smart Grids: Foresights for Social Acceptance," Energies, MDPI, vol. 16(18), pages 1-31, September.

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