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As a key factor, it is vital to mathematically model the electrical efficiency of photovoltaic (PV) systems. This paper investigates the efficient modeling of a 2.5 kW grid-connected PV system, located in Mahan, Kerman, Iran, with respect to different factors like solar radiation, humidity, dust deposition, temperature, and shading. For this aim, different nonlinear models are considered and a curvefitting approach is used to optimally determine the unknown parameters. Over the case study, simulation results show that when the dust deposition is not included in the modeling (solar radiation, ambient temperature, cell temperature, humidity, and shading as inputs), the third-degree polynomial model leads to more accurate result than the other models. By use of the third-degree polynomial model, the value of root mean square error (RMSE) for train and test data sets is obtained 0.0123 and 0.0109, respectively. In this case, the average RMSE value is 0.0116. On the other hand, when dust impact is included in the modeling (solar radiation, ambient temperature, humidity, and number of dusty days as inputs), the accuracy of the exponential model is more than the other ones. In this case, the value of RMSE for both train and test data sets is obtained 0.0018. Sensitivity analysis shows that the system efficiency depends mainly on the radiation, cell temperature, and dust and less on the humidity. [ABSTRACT FROM AUTHOR]