Due to the rapid increase of energy demand and the continuous decrease of renewable energy cost, photovoltaic (PV) installed capacity has increased significantly. The PV power output depends on the available solar irradiance and other meteorological data such as air temperature, wind speed, and relative humidity. The performance of PV panels also depends on the cleaning frequency and maintenance of these panels. Soiling is considered to be a key factor on PV performance in desert areas. The Middle East has one of the highest dust intensity in the world which results in dramatic PV power losses. Therefore, forecasting the power output of PV panels is essential for the development of smart grids and smart metering techniques. In this study, a hybrid Artificial Neural Network (ANN) is developed to forecast the performance of a PV panel. The hybrid ANN is trained on the local weather and solar data as well as different cleaning frequencies. Then, the performance of the hybrid-ANN is compared to that of a conventional ANN. The results are presented in terms of different statistical indices such as the root mean square error (RMSE) and the mean bias error (MBE). The results are used to find the optimal cleaning frequency required for the optimal PV performance.

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