Prediction of photovoltaic power output based on different non-linear autoregressive artificial neural network algorithms

Abstract

Prediction of power output plays a vital role in the installation and operation of photovoltaic modules. In this paper, two photovoltaic module technologies, amorphous silicon and copper indium gallium selenide installed outdoors on the rooftop of the University of Dodoma, located at 6.5738° S and 36.2631° E in Tanzania, were used to record the power output during the winter season. The average data of ambient temperature, module temperature, solar irradiance, relative humidity, and wind speed recorded is used to predict the power output using a non-linear autoregressive artificial neural network. We consider the Levenberg-Marquardt optimization, Bayesian regularization, resilient propagation, and scaled conjugate gradient algorithms to understand their abilities in training, testing and validating the data. A comparison with reference to the performance indices: coefficient of determination, root mean square error, mean absolute percentage error, and mean absolute bias error is drawn for both modules. According to the findings of our investigation, the predicted results are in good agreement with the experimental results. All the algorithms performed better, and the predicted power out of both modules using the Bayesian regularization algorithm is observed to exhibit good processing capabilities compared to the other three algorithms that are evident from the measured performance indices.