PREDICTIVE CAPABILITIES OF A NARX-BASED FORECASTER USED TO PREDICT THE PV PANEL OUTPUT POWER

This paper deals with the predictive capabilities of a NARX-based forecaster used to predict the output power converted with a PV panel in isolated conditions. A time-series based NARX model is proposed and the influence of the meteorological data such as irradiance, ambient temperature and wind speed, and the impact of the training algorithm on the performance of the NARX based forecaster model is studied. The results show that for the studied area, the NARX model trained by three meteorological data as inputs, the output power as output using Bayesian Regularization algorithm gives best performance with a mean squared error of 2.10414e-2.