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Green hydrogen production and prediction using floating photovoltaic panels on wastewater ponds

Author

Listed:
  • Khalifeh Soltani, Sayed Rashid
  • Mostafaeipour, Ali
  • Mishra, Phoolendra
  • Alidoost, Sara
  • Jahangiri, Mehdi
  • Abrisham Kar, Mohammad

Abstract

Air pollution and water scarcity have grown to be global catastrophes. One promising renewable energy source that can help with both of these issues is the floating photovoltaic (FPV) system, particularly when paired with a clean hydrogen generation system. The present study employed an artificial neural network (ANN) model to predict the hydrogen output of an electrolysis process powered by a floating PV system. The Yazd wastewater pond, which experiences seasonal and annual variations in water level, was chosen as the site for this study. The pond is located in the north of Yazd city, in the center of Iran, in a dry and desert area. The amount of electrical energy and hydrogen that can be produced by the system was estimated with the help of PVsyst and Homer software. The model's input data consisted of meteorological and solar radiation data retrieved from NASA, Meteonorm, and local airport meteorological services databases. The model's inputs were the independent variables. The output of the model in the curve fitting part of model development, which was carried out in MATLAB, was the pond's surface area. In terms of prediction accuracy, the Levenberg-Marquardt approach performed the best of all the techniques used to train the ANN model. The electricity output of the FPV system on the Yazd wastewater pond from 2024 to 2028 was estimated. The estimated hydrogen outputs vary from a minimum of 2361 tons in December 2024 to 3626 tons in November 2028.

Suggested Citation

  • Khalifeh Soltani, Sayed Rashid & Mostafaeipour, Ali & Mishra, Phoolendra & Alidoost, Sara & Jahangiri, Mehdi & Abrisham Kar, Mohammad, 2025. "Green hydrogen production and prediction using floating photovoltaic panels on wastewater ponds," Renewable Energy, Elsevier, vol. 243(C).
    • Handle: RePEc:eee:renene:v:243:y:2025:i:c:s0960148125002162
    DOI: 10.1016/j.renene.2025.122554
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    References listed on IDEAS

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