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International Journal of Photoenergy
Volume 2015 (2015), Article ID 413654, 10 pages
Review Article

Hybrid Neural Network Approach Based Tool for the Modelling of Photovoltaic Panels

Department of Engineering, Roma Tre University, Via Vito Volterra 62, 00146 Rome, Italy

Received 19 November 2014; Revised 15 January 2015; Accepted 17 January 2015

Academic Editor: Cheuk-Lam Ho

Copyright © 2015 Antonino Laudani et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A hybrid neural network approach based tool for identifying the photovoltaic one-diode model is presented. The generalization capabilities of neural networks are used together with the robustness of the reduced form of one-diode model. Indeed, from the studies performed by the authors and the works present in the literature, it was found that a direct computation of the five parameters via multiple inputs and multiple outputs neural network is a very difficult task. The reduced form consists in a series of explicit formulae for the support to the neural network that, in our case, is aimed at predicting just two parameters among the five ones identifying the model: the other three parameters are computed by reduced form. The present hybrid approach is efficient from the computational cost point of view and accurate in the estimation of the five parameters. It constitutes a complete and extremely easy tool suitable to be implemented in a microcontroller based architecture. Validations are made on about 10000 PV panels belonging to the California Energy Commission database.