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The prediction of a photovoltaic (PV) energy production over time is considered the major challenge to integrate it with the utilize grid. This is because it is affected by many factors, including geographical locations and weather conditions. Hence, accurately forecasting PV generation is a crucial stage for ensuring a grid stability. In this paper, several studies are discussed between 2015 to 2023 based on various term forecasting conditions. Then, a neural network (NN) is employed to forecast a medium-term PV power generation by gathering meteorological data specific to the southern region of Iraq. The proposed NN model based on Python language is trained to find the PV power prediction for various seasons of the year using solar radiation and surrounding temperature of weather condition tests. While, the MATLAB Simulink model is designed to address the PV actual power for the same tests. The results show that the root mean square error of a PV generation test at autumn season provides the lowest percentage of 92.2119 W when compared with the other three seasons
Radhi et al. (Sun,) studied this question.