Forecasting the Cell Temperature of PV Modules with an Adaptive System

Abstract

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 energy. 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. Furthermore, when photovoltaic (PV) systems replace the traditional building envelope materials and they are fully integrated (building integrated photovoltaic (BIPV)), it is very important to correctly assess their thermal behaviour. The determination of the operating temperature T c 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 T c 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 simplifications nor physical assumptions: on the contrary, the ANN is a black box that learn from actual data, allowing to obtain good results. In the paper is described the ANN that obtained the best performances: 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.