Prediction of electrical energy generation from photovoltaic plants with NARX neural network (#1078)

Authors

Arellanos-Tafur, Elmer

Rojas Arquiñego, Felix Erasmo

Damas Niño, Marcelo Nemesio

Abstract

This research presents the accuracy with which the NARX neural network predicts the generation of electrical energy from photovoltaic plants. The study employed a correlational design, which facilitated the description of the relationship between two variables: X = NARX Neural Networks and Y = Accuracy of Electrical Energy Generation Prediction from Photovoltaic Plants. The prediction consisted of future values of a time series of electrical energy generated by the photovoltaic plants y(t) from the past values of two time series: previously generated electrical energy and past values of solar radiation received during the same period x(t). Two cases were analyzed following this sequence: construction of the neural network, training, validation, and testing of the neural network to achieve the prediction. Finally, the prediction accuracy was evaluated through linear regression analysis, using the correlation coefficient “r” between the outputs and the targets as an indicator. This indicated how well the variation in the output was related to the targets, determining the level of accuracy.