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In efforts to reduce emissions, the production of green energy emerges as an effective solution. This study uses a wind-to-hydrogen system to evaluate the potential for green hydrogen production in Tan-Tan City, Morocco. The system is equipped with a 63 MW wind farm and six electrolyzer modules. The study has two objectives: firstly, to estimate the amount of hydrogen produced from January to December 2023 using this solution; secondly, to use the data generated by this system to train models and predict hydrogen production from January to April 2024. We employ two algorithms, Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN), for this purpose. The LSTM model demonstrated superior performance over the RNN model, as measured by the R-squared (R2) value, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) metrics. Additionally, we compared these two algorithms and concluded why the LSTM performs better than the RNN in our case study. This study and its results can provide a strong foundation for future research.

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

  • 会议论文

  • 发表日期：

    2025年

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  • Mohamed Yassine Rhafes,
  • Omar Moussaoui,
  • Maria Simona Raboaca,
  • Abdelkader Betari

摘要

In efforts to reduce emissions, the production of green energy emerges as an effective solution. This study uses a wind-to-hydrogen system to evaluate the potential for green hydrogen production in Tan-Tan City, Morocco. The system is equipped with a 63 MW wind farm and six electrolyzer modules. The study has two objectives: firstly, to estimate the amount of hydrogen produced from January to December 2023 using this solution; secondly, to use the data generated by this system to train models and predict hydrogen production from January to April 2024. We employ two algorithms, Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN), for this purpose. The LSTM model demonstrated superior performance over the RNN model, as measured by the R-squared (R2) value, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) metrics. Additionally, we compared these two algorithms and concluded why the LSTM performs better than the RNN in our case study. This study and its results can provide a strong foundation for future research.