A radial basis function neural network based approach for the electrical characteristics estimation of a photovoltaic module

TL;DR

This paper presents an RBF neural network model to accurately predict the electrical output of photovoltaic modules under varying conditions, using only irradiation and temperature data for training.

Contribution

It introduces an RBF neural network approach for PV output prediction that improves accuracy by accounting for parameter changes at different operating conditions.

Findings

The RBFNN accurately predicts I--V and P--V curves.

Simulation and experimental results validate the model's effectiveness.

The model requires only irradiation and temperature data for training.

Abstract

The design process of photovoltaic (PV) modules can be greatly enhanced by using advanced and accurate models in order to predict accurately their electrical output behavior. The main aim of this paper is to investigate the application of an advanced neural network based model of a module to improve the accuracy of the predicted output I--V and P--V curves and to keep in account the change of all the parameters at different operating conditions. Radial basis function neural networks (RBFNN) are here utilized to predict the output characteristic of a commercial PV module, by reading only the data of solar irradiation and temperature. A lot of available experimental data were used for the training of the RBFNN, and a backpropagation algorithm was employed. Simulation and experimental validation is reported.