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International Journal of Photoenergy
Volume 2013 (2013), Article ID 192854, 10 pages
Research Article

Forecasting the Cell Temperature of PV Modules with an Adaptive System

DEIM, University of Palermo, Viale delle Scienze, Edificio 9, 90128 Palermo, Italy

Received 28 May 2013; Accepted 3 August 2013

Academic Editor: David Worrall

Copyright © 2013 Giuseppina Ciulla 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.


The need to reduce energy consumptions and to optimize the processes of energy production has pushed the technology towards the implementation of hybrid systems for combined production of electric and thermal energies. In particular, recent researches look with interest at the installation of hybrid system PV/T. To improve the energy performance of these systems, it is necessary to know the operating temperature of the photovoltaic modules. The determination of the operating temperature is a key parameter for the assessment of the actual performance of photovoltaic panels. In the literature, it is possible to find different correlations that evaluate the referring to standard test conditions and/or applying some theoretical simplifications/assumptions. Nevertheless, the application of these different correlations, for the same conditions, does not lead to unequivocal results. In this work an alternative method, based on the employment of artificial neural networks (ANNs), was proposed to predict the operating temperature of a PV module. This methodology does not require any simplification or physical assumptions. In the paper is described the ANN that obtained the best performance: a multilayer perception network. The results have been compared with experimental monitored data and with some of the most cited empirical correlations proposed by different authors.