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International Journal of Photoenergy
Volume 2014, Article ID 469701, 10 pages
http://dx.doi.org/10.1155/2014/469701
Research Article

Modelling and Prediction of Photovoltaic Power Output Using Artificial Neural Networks

Department of Electrical and Electronic Engineering, Universiti Putra Malaysia (UPM), 43400 Serdang, Malaysia

Received 9 January 2014; Accepted 26 February 2014; Published 6 April 2014

Academic Editor: Niyaz Mohammad Mahmoodi

Copyright © 2014 Aminmohammad Saberian et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper presents a solar power modelling method using artificial neural networks (ANNs). Two neural network structures, namely, general regression neural network (GRNN) feedforward back propagation (FFBP), have been used to model a photovoltaic panel output power and approximate the generated power. Both neural networks have four inputs and one output. The inputs are maximum temperature, minimum temperature, mean temperature, and irradiance; the output is the power. The data used in this paper started from January 1, 2006, until December 31, 2010. The five years of data were split into two parts: 2006–2008 and 2009-2010; the first part was used for training and the second part was used for testing the neural networks. A mathematical equation is used to estimate the generated power. At the end, both of these networks have shown good modelling performance; however, FFBP has shown a better performance comparing with GRNN.