Solar energy is becoming more and more incorporated into the global power
grid. As a result, enhancing the accuracy of solar energy projections is crucial for
effective power grid planning, control, and operations. A fast, accurate and advanced
estimation method is desperately needed to prevent PV's detrimental consequences on
electricity and energy networks. For the optimum integration of solar technology into
existing power systems, which benefits both grids and station operators, accurate
prediction of solar production is crucial. The purpose of this research is to test the
effectiveness of the machine learning model for projecting PV solar output. Using
ANN in this research, weather parameters with the Power Generation for the next day
appear to have been predicted. The evaluation findings suggest that the models'
accuracy is sufficient to be employed with existing works and their approaches.
Machine learning was shown to be capable of accurately predicting power while
removing the difficulties associated with predicted solar irradiance data in this study.
Keywords: ANN, Machine Learning, PV System, Power System, Solar.
