A Comparative Study on Forecasting Solar Photovoltaic Power Generation Using Artificial Neural Networks

Solar PV power generation is intermittent and results in various grid related issues when integrated to the utility grid. By having prior knowledge of the power generation capabilities of solar photovoltaic (PV) systems, it becomes possible to execute the operation of other power systems in a systematic manner, thereby mitigating grid-related issues arising from the intermittent nature of solar PV power generation. This paper discusses PV power forecasting using Artificial Neural Networks by two different approaches. The results obtained from the selected approaches are presented and discussed. The performance evaluation of the selected methods is carried out in terms of root mean square error (RMSE), mean absolute error (MAE) and R
²
values.