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

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.

An Intelligent Approach to Educational Data: Performance Comparison of the Multilayer Perceptron and the Radial Basis Function Artificial Neural Networks

“…Yılmaz and Özer (2009) proposed an artificial neural network-based pitch angle controller for wind turbines and found that the RBFANN outperformed the MLPANN. Bonanno, Capizzi, Graditi, Napoli, and Tina (2012) studied the electrical characteristics estimation of a photovoltaic module by using the RBFANN and the MLPANN comparatively. Their results showed that the RBFANN-based models achieved superior performance compared to the MLPANN.…”

Precision Modeling: Application of Metaheuristics on Current–Voltage Curves of Superconducting Films

“…Although its computation burden was less than that of the ANN based models, it lacked accuracy-tolerance of 4-6% within actual values as compared to the ANN models having tolerance less than 1% with the actual values. Another attempt based on ANN was presented by Bonanno et al [17]. The authors proposed a radial basis function neural network (RBFNN) and demonstrated that it had a prediction accuracy of about 10 −1 (MSE), which is even lesser than the already existing techniques-let alone our current model based on a genetic algorithm, achieving MSE in the order of 10 −9 .…”

Artificial Neural Network Applied to Estimate the Power Output of BIPV Systems

“…Hence, these techniques are easy to implement but more vulnerable to the accuracy of the few available data points. The latter techniques, including neural network [15,16], differential evolution (DE) [17], genetic algorithm [18], and particle swarm optimization (PSO) [19], etc., usually employ all the experimental data at various operating conditions to extract physical parameters, thus providing a higher confidence level of the extracted parameters. There are several studies about photovoltaic energy in Colombia [20][21][22][23][24] and this one is intended to improve the distributed energy analysis on buildings.…”