Abstract
Solar photovoltaic (PV) is playing a major role in the United Arab Emirates (UAE) smart grid infrastructure. However, one of the challenges facing PV-based energy systems is the dust accumulation on solar panels. Dust accumulation on solar panels results in a high degradation in the output power. The UAE has low intensity rainfall and wind velocity; therefore solar panels must be cleaned manually or using automated cleaning methods. Estimating dust accumulation on solar panels will increase the output power and reduce maintenance costs by initiating cleaning actions only when required. In this paper, the impact of natural dust accumulation on solar panels is investigated using field measurements and regression modeling. Experimental data were collected under various real weather conditions and controlled levels of dust. Moreover, this paper proposes a data-driven approach based on machine learning to estimate the accumulated dust level on solar panels. In this approach, a dust estimation unit based on a regression tree model has been developed to estimate the dust accumulation. This unit is trained using experimental records of solar irradiance, ambient temperature, and the output power generated from solar panels as well as the amount of dust at these conditions. The proposed unit is evaluated through different case studies with a random amount of dust applied to the solar panels to demonstrate the accurate performance of the proposed unit.