Name
Title | ||
|---|---|---|
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 back-propagation algorithm was employed. Simulation and experimental validation is reported. (C) 2012 Elsevier Ltd. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.apenergy.2011.12.085 | APPLIED ENERGY |
Keywords | Field | DocType |
Solar energy,Solar cell,Photovoltaic modules,Circuital models,Radial basis function,Neural networks | Radial basis function,Experimental data,Solar energy,Control engineering,Electronic engineering,Solar cell,Engineering design process,Engineering,Artificial neural network,Backpropagation,Photovoltaic system | Journal |
Volume | ISSN | Citations |
97 | 0306-2619 | 6 |
PageRank | References | Authors |
0.68 | 1 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Francesco Bonanno | 1 | 6 | 1.02 |
| Giacomo Capizzi | 2 | 60 | 11.94 |
| Christian Napoli | 3 | 201 | 24.64 |
| Giorgio Graditi | 4 | 72 | 8.78 |
| Giuseppe M. Tina | 5 | 28 | 3.60 |