A deep learning-enhanced framework for sustainable hydrogen production from solar and wind energy in the Moroccan Sahara: Coastal regions focus - Université de Picardie Jules Verne Accéder directement au contenu
Article Dans Une Revue Energy Conversion and Management Année : 2024

A deep learning-enhanced framework for sustainable hydrogen production from solar and wind energy in the Moroccan Sahara: Coastal regions focus

K. Adeli
  • Fonction : Auteur
M. Nachtane
  • Fonction : Auteur
A. Faik
  • Fonction : Auteur
M. Tarfaoui
  • Fonction : Auteur

Résumé

Aligned with King Mohammed VI's visionary objectives for sustainable development in Morocco, this research introduces a transformative initiative focused on coastal regions. Our study explores the implementation of new integrated wind and photovoltaic (PV) parks, desalination units and hydrogen production presenting a comprehensive solution to address energy, water and environmental challenges. Strategically locating wind and photovoltaic parks in cities like Laayoune, Dakhla, Boujdour and Tarfaya renowned for their abundant renewable resources, contributes significantly to local energy grids and forms integral components of a broader sustainability strategy. Simultaneously, the incorporation of desalination units addresses freshwater scarcity in arid coastal areas. The research delves into the synergy between renewable energy and hydrogen production optimizing efficiency and output through advanced technologies such as artificial neural networks (ANNs) and deep learning algorithms. This innovative approach positions hydrogen as a crucial and versatile energy carrier within a clean energy ecosystem. Our analysis reveals substantial annual electricity production with Laayoune, Dakhla, Boujdour and Tarfaya contributing 339.75 MW, 407.17 MW, 327.66 MW and 266.57 MW respectively. Additionally, specific annual hydrogen outputs are projected at 147.45 tonne, 176.71 tonne, 142.21 tonne and 115.69 tonne for these locations. System efficiency and production costs are estimated to range from 18% to 30%. Utilizing a state-of-the-art Recurrent Neural Network (RNN) model for a ten-year projection. This research envisions a cleaner and more sustainable future for Morocco marked by enhanced energy production, water resilience, and hydrogen innovation.
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Dates et versions

hal-04473283 , version 1 (22-02-2024)

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Citer

K. Adeli, M. Nachtane, A. Faik, A. Rachid, M. Tarfaoui, et al.. A deep learning-enhanced framework for sustainable hydrogen production from solar and wind energy in the Moroccan Sahara: Coastal regions focus. Energy Conversion and Management, 2024, 302, pp.118084. ⟨10.1016/j.enconman.2024.118084⟩. ⟨hal-04473283⟩

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