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|Title:||Artificial neural network-based model for estimating the produced power ofaphotovoltaic module||Authors:||Mellit, Adel ItemCrisRefDisplayStrategy.rp.deleted.icon
Kalogirou, Soteris A.
|Keywords:||ANN;Forecasting;Modelling;Photovoltaic module;Produced power||Category:||Computer and Information Sciences||Field:||Natural Sciences||Issue Date:||1-Dec-2013||Publisher:||Elsevier BV||Source:||Renewable Energy, 2013, Volume 60, Pages 71-78||metadata.dc.doi:||http://dx.doi.org/10.1016/j.renene.2013.04.011||Abstract:||In this paper, a methodology to estimate the profile of the produced power of a 50Wp Si-polycrystalline photovoltaic (PV) module is described. For this purpose, two artificial neural networks (ANNs) have been developed for use in cloudy and sunny days respectively. More than one year of measured data (solar irradiance, air temperature, PV module voltage and PV module current) have been recorded at the Marmara University, Istanbul, Turkey (from 1-1-2011 to 24-2-2012) and used for the training and validation of the models. Results confirm the ability of the developed ANN-models for estimating the power produced with reasonable accuracy. A comparative study shows that the ANN-models perform better than polynomial regression, multiple linear regression, analytical and one-diode models. The advantage of the ANN-models is that they do not need more parameters or complicate calculations unlike implicit models. The developed models could be used to forecast the profile of the produced power. Although, the methodology has been applied for one polycrystalline PV module, it could also be generalized for large-scale photovoltaic plants as well as for other PV technologies.||URI:||http://ktisis.cut.ac.cy/handle/10488/9795||ISSN:||09601481||Rights:||© 2013 Elsevier Ltd.||Type:||Article|
|Appears in Collections:||Άρθρα/Articles|
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