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2025 | OriginalPaper | Chapter

Analysis and Prediction of Green Hydrogen Production Potential Using Deep Learning in Tan-Tan

Authors : Mohamed Yassine Rhafes, Omar Moussaoui, Maria Simona Raboaca, Abdelkader Betari

Published in: Proceedings of the 4th International Conference on Electronic Engineering and Renewable Energy Systems—Volume 1

Publisher: Springer Nature Singapore

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Abstract

This chapter delves into the urgent global need for sustainable energy solutions, focusing on green hydrogen production as a viable alternative to fossil fuels. The study centers on Tan-Tan, Morocco, a region with high wind speeds, making it ideal for wind-to-hydrogen systems. The research employs deep learning algorithms, specifically LSTM and RNN models, to predict hydrogen production accurately. The dataset, sourced from NASA POWER Data Access Viewer and adapted by the National Research and Development Institute for Cryogenic and Isotopic Technologies, includes comprehensive meteorological and energy output data. The chapter provides a detailed comparison of the models' performance, highlighting the superior accuracy of the LSTM model in handling long-term time-series data. The findings underscore the significant potential of green hydrogen production in Tan-Tan, with a total of 6,693,375.98 normal cubic meters produced from January to December 2023. The study also predicts hydrogen production for the first four months of 2024, demonstrating the practical application of AI in renewable energy forecasting. The chapter concludes by suggesting the extension of this approach to other regions in Morocco, paving the way for broader adoption of green hydrogen technologies.

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Title: Analysis and Prediction of Green Hydrogen Production Potential Using Deep Learning in Tan-Tan
Authors: Mohamed Yassine Rhafes
Omar Moussaoui
Maria Simona Raboaca
Abdelkader Betari
Publisher: Springer Nature Singapore
Book: Proceedings of the 4th International Conference on Electronic Engineering and Renewable Energy Systems—Volume 1
Print ISBN: 978-981-9606-43-6

Electronic ISBN: 978-981-9606-44-3

Copyright Year: 2025
DOI: https://doi.org/10.1007/978-981-96-0644-3_49