A non-linear auto-regressive exogenous method to forecast the photovoltaic power output. (April 2020) - Louzazni, Mohamed
Highlights: Forecasting of photovoltaic power production installed in Belbis, Egypt. Using the nonlinear autoregressive with exogenous model (NARX) based in neural network times series and Levenberg-Marquardt training algorithm. The performance of the used technique has been studied as a function of training data sets, error definitions and based on photovoltaic plant experimental data. The obtained results shown that the forecasted power obtained by NARX method give a high correlation with the experimental data. Abstract: This paper deal about the prediction of SunModule SW 175 monocrystalline photovoltaic (PV) module power output installed in Belbis, Egypt. The proposes prediction model forecast one month using a non-linear auto-regressive exogenous method, based in neural network times series and Levenberg-Marquardt training algorithm. NARX neural network are powerful to solve several problems and popular in nonlinear control applications. The NARX model is choosing for rapid training and convergence speed and strong representativeness and is characterized by favourable dynamics and resistance to interference. Besides, the exactitude of NARX method has examined as a function of training data sets, error definitions relying on experimental data of a PV framework. The predicted power acquired by the NARX method gives a high correlativity with the experimental data and comparatively low errors. The forecast of output power obtained with the NARX method are compared with neural network and experimentally measured data. The obtained result is very accurate in R 2 coefficient 99.47% and MSE = 20.5753% compared to NARX-Bayesian R 2 = 99.47 and RMSE = 21.71%. Generally, the execution and exactness of the results are exceedingly relying upon the climate condition, and the R 2 took a low value if the user data in series analysis are not very accurate.