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Artificial Intelligence Prediction Analysis of Daily Power Photovoltaic Bifacial Modules in Two Moroccan Cities

Author

Listed:
  • Salma Riad

    (Laboratoire de Matière Condensée et Sciences Interdisciplinaires URL-CNRST-17, Faculty of Sciences, Mohammed V University in Rabat, Rabat BP 1014, Morocco)

  • Naoual Bekkioui

    (Laboratoire de Matière Condensée et Sciences Interdisciplinaires URL-CNRST-17, Faculty of Sciences, Mohammed V University in Rabat, Rabat BP 1014, Morocco)

  • Merlin Simo-Tagne

    (INRAE, LERMAB, ERBE—F, University of Lorraine, 27 rue Philippe Seguin, CS 60036, 88026 Epinal, France)

  • Ndukwu Macmanus Chinenye

    (Department of Agricultural and Bio-Resources Engineering, Michael Okpara University of Agriculture, Umuahia P.M.B. 7267, Abia State, Nigeria)

  • Hamid Ez-Zahraouy

    (Laboratoire de Matière Condensée et Sciences Interdisciplinaires URL-CNRST-17, Faculty of Sciences, Mohammed V University in Rabat, Rabat BP 1014, Morocco)

Abstract

This study aimed to train and validate two artificial neural network (ANN) models, one with four hidden layers and the other with five hidden layers, to predict the daily photovoltaic power output of a 20 Kw photovoltaic power plant with bifacial photovoltaic modules with tilt angle variation from 0° to 90° in two Moroccan cities, Ouarzazate and Oujda. To validate the two proposed models, photovoltaic power data calculated using the System Advisor Model (SAM) software version 2023.12.17 were employed to predict the average daily power of the photovoltaic plant for December, utilizing MATLAB software Version R2020a 9.8, and for the tilt angles corresponding to the latitudes of the two cities studied. The results differ from one model to another according to their mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R 2 ) values. The artificial neural network model with five hidden layers obtained better results with a R 2 value of 0.99354 for Ouarzazate and 0.99836 for Oujda. These two proposed models are trained using the Levenberg Marquardt (LM) optimizer, which is proven to be the best training procedure.

Suggested Citation

  • Salma Riad & Naoual Bekkioui & Merlin Simo-Tagne & Ndukwu Macmanus Chinenye & Hamid Ez-Zahraouy, 2025. "Artificial Intelligence Prediction Analysis of Daily Power Photovoltaic Bifacial Modules in Two Moroccan Cities," Sustainability, MDPI, vol. 17(15), pages 1-22, July.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:15:p:6900-:d:1712819
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    References listed on IDEAS

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    1. Ma, Tao & Yang, Hongxing & Lu, Lin, 2014. "Solar photovoltaic system modeling and performance prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 36(C), pages 304-315.
    2. Javier López Gómez & Ana Ogando Martínez & Francisco Troncoso Pastoriza & Lara Febrero Garrido & Enrique Granada Álvarez & José Antonio Orosa García, 2020. "Photovoltaic Power Prediction Using Artificial Neural Networks and Numerical Weather Data," Sustainability, MDPI, vol. 12(24), pages 1-18, December.
    3. Emad M. Ahmed & Mokhtar Aly & Manar Mostafa & Hegazy Rezk & Hammad Alnuman & Waleed Alhosaini, 2022. "An Accurate Model for Bifacial Photovoltaic Panels," Sustainability, MDPI, vol. 15(1), pages 1-27, December.
    4. Piotr Olczak & Małgorzata Olek & Dominika Matuszewska & Artur Dyczko & Tomasz Mania, 2021. "Monofacial and Bifacial Micro PV Installation as Element of Energy Transition—The Case of Poland," Energies, MDPI, vol. 14(2), pages 1-22, January.
    5. Graditi, G. & Ferlito, S. & Adinolfi, G., 2016. "Comparison of Photovoltaic plant power production prediction methods using a large measured dataset," Renewable Energy, Elsevier, vol. 90(C), pages 513-519.
    6. Lioua Kolsi & Sameer Al-Dahidi & Souad Kamel & Walid Aich & Sahbi Boubaker & Nidhal Ben Khedher, 2022. "Prediction of Solar Energy Yield Based on Artificial Intelligence Techniques for the Ha’il Region, Saudi Arabia," Sustainability, MDPI, vol. 15(1), pages 1-15, December.
    7. Polo, Jesús & Alonso-Abella, Miguel & Marcos, Ana & Sanz-Saiz, Carlos & Martín-Chivelet, Nuria, 2024. "On the use of reference modules in characterizing the performance of bifacial modules for rooftop canopy applications," Renewable Energy, Elsevier, vol. 220(C).
    8. Jarimi, Hasila & Al-Waeli, Ali H.A. & Razak, Tajul Rosli & Abu Bakar, Mohd Nazari & Fazlizan, Ahmad & Ibrahim, Adnan & Sopian, Kamaruzzaman, 2022. "Neural network modelling and performance estimation of dual-fluid photovoltaic thermal solar collectors in tropical climate conditions," Renewable Energy, Elsevier, vol. 197(C), pages 1009-1019.
    9. Amir A. Abdallah & Maulid Kivambe & Brahim Aïssa & Benjamin W. Figgis, 2023. "Performance of Monofacial and Bifacial Silicon Heterojunction Modules under Desert Conditions and the Impact of PV Soiling," Sustainability, MDPI, vol. 15(10), pages 1-13, May.
    10. Ganesan, K. & Winston, D. Prince & Nesamalar, J. Jeslin Drusila & Pravin, M., 2024. "Output power enhancement of a bifacial solar photovoltaic with upside down installation during module defects," Applied Energy, Elsevier, vol. 353(PA).
    Full references (including those not matched with items on IDEAS)

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