Multi-criteria Modeling of Photovoltaic Module Soiling Losses in a Grid-Connected Power Plant within the Sudano-Sahelian Zone (Burkina Faso) Using Artificial Neural Networks and Linear Regression
Abdoulaye Kaboré *
Laboratoire de Physique et de Chimie de l’Environnement/École Doctorale Sciences et Technologies/Université Joseph KI-ZERBO, 03 BP : 7021 Ouagadougou 03, Burkina Faso.
Kayaba Haro
Centre National de la Recherche Scientifique et Technologique / Institut de Recherche en Sciences Appliquées et Technologies (CNRST/IRSAT), 03 BP :7047 Ouagadougou 03, Burkina Faso.
Tongonmanegde Leonard Ouedraogo
Laboratoire de Physique et de Chimie de l’Environnement/École Doctorale Sciences et Technologies/Université Joseph KI-ZERBO, 03 BP : 7021 Ouagadougou 03, Burkina Faso.
Sidiki Zongo
Laboratoire de Physique et de Chimie de l’Environnement/École Doctorale Sciences et Technologies/Université Joseph KI-ZERBO, 03 BP : 7021 Ouagadougou 03, Burkina Faso.
Boubou Bagré
Université Nazi BONI, Bobo-Dioulasso, Burkina Faso.
Christian Tubreoumya Guy
Laboratoire de Physique et de Chimie de l’Environnement/École Doctorale Sciences et Technologies/Université Joseph KI-ZERBO, 03 BP : 7021 Ouagadougou 03, Burkina Faso.
Bowendkuni Armand Korsaga
Laboratoire de Physique et de Chimie de l’Environnement/École Doctorale Sciences et Technologies/Université Joseph KI-ZERBO, 03 BP : 7021 Ouagadougou 03, Burkina Faso.
Issoufou Ouarma
Centre Universitaire de Banfora, Université Nazi BONI, Bobo-Dioulasso, Burkina Faso.
Daniel M Westervelt
Lamont-Doherty Earth Observatory, Columbia University, Palisades, New York, 10964, United States and Climate School, Columbia University, New York, 10027, United States.
Samuel Ouoba
Laboratoire de Physique et de Chimie de l’Environnement/École Doctorale Sciences et Technologies/Université Joseph KI-ZERBO, 03 BP : 7021 Ouagadougou 03, Burkina Faso.
Antoine Béré
Laboratoire de Physique et de Chimie de l’Environnement/École Doctorale Sciences et Technologies/Université Joseph KI-ZERBO, 03 BP : 7021 Ouagadougou 03, Burkina Faso.
*Author to whom correspondence should be addressed.
Abstract
Soiling of photovoltaic modules is a major factor affecting the performance of solar power plants, particularly in dry and dust-prone environments. This study developed and evaluated predictive models for estimating the soiling losses (SL) of photovoltaic modules at the Zagtouli grid-connected solar power plant in Burkina Faso. Field-measured environmental and meteorological variables were used to examine relationships between local operating conditions and module soiling. After variable screening, five predictors were retained for modelling: exposure duration, ambient temperature, wind speed, relative humidity and PM10 concentration. Two modelling approaches were compared: multiple linear regression (MLR) and an artificial neural network (ANN). The MLR model provided a useful baseline and explained approximately 94% of the variability in the soiling losses. However, its linear structure limited its ability to represent more complex interactions among climatic and particulate variables. The ANN model, developed using a 5-15-1 multilayer perceptron architecture, showed stronger predictive performance. The optimised model achieved coefficients of determination of 98.42% during training and 98.45% during validation, with low error values for the mean square error (MSE = 0.015), root mean square error (RMSE = 0.122) and mean absolute error (MAE = 0.086). These findings indicate that exposure duration was the dominant predictor, while the nonlinear modelling approach better represented the combined effects of meteorological and particulate factors. The proposed model provides a site-specific basis for understanding photovoltaic soiling behaviour in a Sudano-Sahelian environment and may support more informed maintenance planning for grid-connected photovoltaic plants operating under similar climatic conditions.
Keywords: Photovoltaic soiling, soiling losses, artificial neural network, multiple linear regression, PM10 concentration, exposure duration, predictive modelling, Zagtouli solar plant, Sudano-Sahelian zone, maintenance planning.