Artificial neural network modelling and experimental verification of the operating current of mono-crystalline photovoltaic modules

Yükleniyor...
Küçük Resim

Tarih

2011

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

Pergamon-Elsevier Science Ltd

Erişim Hakkı

info:eu-repo/semantics/closedAccess

Özet

This article presents the artificial neural network modelling of the operating current of a 120 Wp of mono-crystalline photovoltaic module. As an alternative method to analytical modelling approaches, this study uses the advantages of neural networks such as no required knowledge of internal system parameters, less computational effort and a compact solution for multivariable problems. Generalised regression neural network model is used in the present article to predict the operating current of the photovoltaic module. To show its merit, the current predicted from the artificial neural network modelling is compared to that from the analytical model. The five-parameter analytical model is drawn from the equivalent electrical circuit that includes light-generated current, diode reverse saturation current, and series and shunt resistances. The operating current predicted from both the neural and analytical models are compared to the measured current. Results have shown that the artificial neural network modelling provides a better prediction of the current than the five-parameter analytical model. (C) 2011 Elsevier Ltd. All rights reserved.

Açıklama

Anahtar Kelimeler

Artificial Neural Network Modelling, Modelling Photovoltaic Cells, Analytical Modelling, Generalised Regression Neural Network

Kaynak

Solar Energy

WoS Q Değeri

Q2

Scopus Q Değeri

Q1

Cilt

85

Sayı

10

Künye