Artificial neural network modeling and sensitivity analysis for soiling effects on photovoltaic panels in Morocco. (March 2019) - Laarabi, B.; May Tzuc, O.; Dahlioui, D.; Bassam, A.; Flota-Bañuelos, M.; Barhdadi, A.
Abstract: In the present work, an Artificial Neural Network (ANN) methodology for studying and modeling the soiling effect on solar photovoltaic (PV) glass is presented. To perform the study, a solar PV glazing was exposed outdoor at the home solar energy platform of Physic of Semi-conductors and Solar Energy research structure (PSES) at Mohammed V University in Rabat, Morocco. Regular measurements from April 20, to December 31, 2016, were carried out to monitor the soiling rate changes over time. Meteorological data were used as input variables for ANN modeling. The model performance was evaluated using a statistical comparison between experimental and simulated values. Results show that the implementation of Levenberg-Marquardt backpropagation algorithm, and the active functions Tansig, and Purline achieve the best estimations (R 2  = 0.928) in an ANN architecture 6-35-1. Additionally, a sensitivity analysis approach was employed to determine the effect of input parameters on model output and the behavior of the model with the variation of each input parameter. Sensitivity analysis results indicate that the most influential parameter for PV soiling rate was the relative humidity, followed by wind direction. The ANN model coupled with sensitivity analysis show be a promising framework for its application in smart sensors on cleaning systems for PV modules to improve their operational efficiency. Highlights: An artificial neural network methodology has been proposed for modeling soiling rate on solar photovoltaic glass. Artificial neural network was trained and testing using environmental variables. Statistical criteria were employed to measure the error between experimental and the predicted results. A sensitivity analysis approach was employed to determine the effect of input parameter on model output.