## 1 Introduction
Moreover, they concluded that such storms affect the PV system performance significantly. During the period from February to May in Southern Libya, the effect of dust on PV performance was analyzed experimentally [17]. They concluded that there is a performance loss due to dust which ranges between 2% and 2.5%. Inspecting the published work discussed in the area of investigating the effects of operating and ambient conditions on the performance of the PV system, it is concluded intuitively that the effect of ambient temperature, irradiance, cell temperature and wind speed on PV systems depends on geographical location and environmental and meteorological conditions. Researchers around the world strive for better comprehension of the impact of these parameters on PV systems. Understanding the effect of these parameters is realized by designing robust and reliable theoretical models and/or conducting experiments. For instance, correlations of solar electrical efficiency and power dependence on temperature were reviewed in [18]. [PERSON] et al. [19] reported that the power and efficiency of PV modules drop as the ambient temperature increases. It was found in the work conducted by [19] for PV modules equipped with fins at the backside that the maximum power decreases as the ambient temperature increases. Moreover, an experimental study to investigate the effect of the dry bulb temperature on the performance of the thin-film PV module was carried out by [20], and it was concluded that the dry bulb temperature has a significant effect on the open circuit voltage and less effect on the short circuit current. Additionally, effects of the ambient temperature and wind speed on the PV module temperatures were simulated in a parametric study in the work presented by [21], it was found that the module temperature decreases with wind speed at constant ambient temperature. Finally, in the experimental work conducted in Abu-Dhabi to investigate the effects of solar irradiance, wind speed, relative humidity and ambient temperature on a thin film grid-connected PV system, presented in [22], it was concluded that both the open-circuit voltage and short-circuit current drop as the ambient temperature increases. Based on the above-mentioned literature review, and since soiling and dust have a significant impact on the PV performance, it is crucial to design models that can predict PV performance. The complexity of these models increases as the number of parameters and factors taken into consideration increases. For example, [PERSON] et al. [PERSON] et al. (2018) proposed a neural network model to predict the PV power output as a function of the chemical, physical and spectral characteristics of the soil, and they concluded that the neural network hybrid performed well compared to other networks. In the work of [PERSON] et al. [PERSON] et al. (2018), both neural network and regression models were proposed to predict the PV power loss resulting from artificial soiling. They concluded that the regression model fits well for a specific irradiance range and some types of soil, while the neural network model succeeds in other ranges of irradiance and different types of soil. In Italy, a study was conducted by [PERSON] et al. [PERSON] et al. (2018) to investigate the effect of soil on PV power loss, and two models to predict the power loss, namely the Bayesian neural network and polynomial regression, were proposed. It was shown that the results of both models show excellent agreement, with power losses of 5.25% and 1.01% for PV systems installed on sandy and green lands, respectively. In order to investigate the effect of the particle size on the PV soiling losses, in a study conducted by [PERSON] et al. [PERSON] et al. (2018), a multiple variable regression model as a function of the irradiance, tilt angle and particle size is proposed. It was shown that for a tilt angle of 18, and when particle sizes of less than 75 mm diameter are dominant, the optimal tilt angle deviates by 4\({}^{\circ}\) from the optimal tilt angle of the clean module.