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

Salma Riad, Naoual Bekkioui (), Merlin Simo-Tagne, Ndukwu Macmanus Chinenye and Hamid Ez-Zahraouy
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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

Sustainability, 2025, vol. 17, issue 15, 1-22

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.

Keywords: artificial neural network; bifacial photovoltaic modules; daily photovoltaic power; tilt angle; prediction; System Advisor Model SAM (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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