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

Fatemehsadat Mirshafiee, Emad Shahbazi, Mohadeseh Safi and Rituraj Rituraj ()
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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

Energies, 2023, vol. 16, issue 1, 1-20

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.

Keywords: hydrogen production; renewable energy; green energy; simulation; FLOW-3D; electrical power (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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