Estimating the maximum power of a High Concentrator Photovoltaic (HCPV) module using an Artificial Neural Network

F. Almonacid, Eduardo F. Fernández, P. Rodrigo, P.J. Pérez-Higueras and C. Rus-Casas

Energy, 2013, vol. 53, issue C, 165-172

Abstract: The peculiarities of High Concentrator Photovoltaic (HCPV) modules make it very difficult to estimate their output. There are some methods to estimate the maximum power of an HCPV module which provide good results, but they are not easy to apply for the following reasons: a) they do not offer a comprehensive explanation of the entire procedure, b) they need some intrinsic parameters of MJ (multi-junction) solar cells which are difficult to obtain, c) it is necessary to have either complex and expensive devices for indoor measurements, and/or an accurate and complete set of outdoor measurements. This paper is intended to propose a model based on Artificial Neural Networks (ANNs) to predict the maximum power of an HCPV module using easily measurable atmospheric parameters. To this end, a group of atmospheric parameters together with the maximum power of an HCPV module have been measured throughout a whole year at a research centre located in the south of Spain. The results showed that using atmospheric parameters, the proposed ANN is capable of estimating the maximum power of an HCPV module with a mean square root error of 3.29%. This model could be extended to other modules and other places.

Keywords: Artificial Neural Network; High Concentrator Photovoltaic Module; Maximum power prediction (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (21)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:53:y:2013:i:c:p:165-172

DOI: 10.1016/j.energy.2013.02.024

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