Characterisation of Si-crystalline PV modules by artificial neural networks

F. Almonacid, C. Rus, L. Hontoria, M. Fuentes and G. Nofuentes

Renewable Energy, 2009, vol. 34, issue 4, 941-949

Abstract: In the photovoltaic field, manufacturers provide ratings for PV modules for conditions referred to as standard test conditions (STC). However, these conditions rarely occur outdoors, so the usefulness and applicability of the indoors' characterisation in standard test conditions of PV modules are a controversial issue. Therefore, to carry out photovoltaic engineering well, a suitable characterisation of PV module electrical behaviour (V–I curves) is necessary. The IDEA Research Group from Jaén University has developed a method based on artificial neural networks (ANNs) to electrical characterisation of PV modules. An ANN has been developed which is able to generate V–I curves of Si-crystalline PV modules for any irradiance and module cell temperature. The results show that the proposed ANN introduces a good accurate prediction for Si-crystalline PV modules' performance when compared with the measured values.

Keywords: PV modules; Si-crystalline; Artificial neural network (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (31)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:34:y:2009:i:4:p:941-949

DOI: 10.1016/j.renene.2008.06.010

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