Forecasting energy production of a PV system connected by using NARX neural network model

Applying artificial neural network techniques to forecast the electricity production of photovoltaic (PV) power plants is a novel concept. A reliable analytical model for calculating the energy output of a grid-connected solar plant is very difficult to establish because of hourly, daily, and seasonal variations in climate. The current study estimated and predicted the energy production of a connected PV system that was installed in Cairo, Egypt (30.13 degrees N and 31.40 degrees E) using an artificial neural network. Four seasons' worth of data (summer, autumn, winter, and spring) were methodically assessed using information from the climate database. The parameters that had an impact on the electrical data of PV modules included meteorological and irradiation variables, energy output, and the user's needs used to verify the NARX feedback neural networks. Prediction performance metrics were obtained, such as the correlation coefficient (R) and root mean square error (RMSE). The observed correlation coefficient ranged from 99% to 100%, indicating that the expected results are verified, while the mean error fluctuates very little.