LAUSR

Abstract Photovoltaic technology is still developing in the MENA region. Nevertheless, soiling remains a major cause for performance loss in PV Modules. In this work, the soiling rate is modeled as a function of environmental data with several modeling methods such as the multiple linear regression model (MLR), multiple linear regression with interaction model (MLRWI), the mathematical model generated by the response surface methodology (RSM) and Artificial Neural Networks (ANNs), by using one-year of ground measurements from an amorphous array with a capacity of 2.16 kWc. The experiment is carried under a semi-arid climate at Green Energy Park research facility (Benguerir, Morocco). The dust analysis was carried out by Scanning Electron Microscope (SEM), Energy Dispersive X-Ray Spectroscopy (EDS), and X-ray fluorescence (XRF) in two periods (December 2017 and June 2018) in order to define the mineralogy and morphology of our dust samples. The results of this study show that, the daily energy drop reaches 0.43 kWh/day and 0.61 kW/h/day in the dry period and 0.03 kW/h/day in the rainy period, where the expected produced energy is 5.59 KWh/day. The daily performance ratio drop reaches an average of 6.1%/day and 1.6%/day in the dry and rainy period respectively. During the dry period the soiling ratio reaches an average of 0.35%/day. The MLR method marked the lowest correlation with r2 = 0.23, this correlation improved to reach r2 = 0.48 by using the MLRWI method. ANN model shows the best performance and accuracy with r2 = 0.813 and around 0.026 in the RMSE indicator.