Predicting Power and Hydrogen Generation of a Renewable Energy Converter Utilizing Data-Driven methods; a Sustainable Smart Grid Case Study

EasyChair Preprint 9520

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 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.

Keyphrases: FLOW-3D, Hydrogen production, electrical power, green energy, renewable energy, simulation

@booklet{EasyChair:9520,
  author    = {Fatemehsadat Mirshafiee and Emad Shahbazi and Mohadeseh Safi and Rituraj Rituraj},
  title     = {Predicting Power and Hydrogen Generation of a Renewable Energy Converter Utilizing Data-Driven methods; a Sustainable Smart Grid Case Study},
  howpublished = {EasyChair Preprint 9520},
  year      = {EasyChair, 2023}}