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|Title:||Review of techniques based on artificial neural networks for the electrical characterization of concentrator photovoltaic technology||Authors:||Almonacid, Florencia
Fernandez, Eduardo F.
Kalogirou, Soteris A.
|Keywords:||Artificial neural networks
|Issue Date:||1-Aug-2017||Publisher:||Elsevier Ltd||Source:||Renewable and Sustainable Energy Reviews, 2017, Volume 75, Pages 938-953||Abstract:||Concentrator photovoltaics (CPV) is considered to be one of the most promising renewable energy components that could lead to a reduction on the dependence on fossil fuels. The aim of CPV technology is to lower the cost of the system by reducing the semiconductor material, and replacing it by cheap optical devices that concentrate the light received from the sun on a small-size solar cell. The electrical characterization of devices based on this technology however, is inherently different and more complex than that of the traditional PV devices. Due to the advantages offered by the Artificial Neuron Networks (ANNs) to solve complex and non-linear problems, and the great level of complexity of electrical modelling of CPV devices, in recent years, several authors have applied a variety of ANNs to solve issues related to CPV technology. In this paper, a review of the ANNs developed to address various topics related with both, low and high concentrator photovoltaics, is presented. Moreover, a review of the ANN-based models to predict the main environmental parameters that affect the performance of CPV systems operating outdoors is also provided. Published papers presented show the potential of the ANNs as a powerful tool for modelling the CPV technology.||URI:||http://ktisis.cut.ac.cy/handle/10488/10099||ISSN:||13640321||Rights:||© 2016 Elsevier Ltd|
|Appears in Collections:||Άρθρα/Articles|
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