Solar power plant generation forecasting using NARX neural network model: A case study

New technologies have been developed and adopted to generate energy from renewable sources satisfy the increasing demand without causing environmental damage. However, estimating power output of inherently intermittent, weather-driven, non-dispatchable is a major scientific societal concern. In this study, neural network model enable short-to-middle term forecasts photovoltaic (PV) system provided. Using historical weather generation data, non-linear autoregressive with exogenous input (NARX) built forecast output. The performance then analyzed by different statistical evaluation parameters. It shown that PV estimation method can be successfully employed.