Journal
APPLIED ENERGY
Volume 97, Issue -, Pages 956-961Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2011.12.085
Keywords
Solar energy; Solar cell; Photovoltaic modules; Circuital models; Radial basis function; Neural networks
Categories
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available