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Modelling and prediction of photovoltaic power output using artificial neural networks


Saberian, Aminmohammad and Hizam, Hashim and Mohd Radzi, Mohd Amran and Ab Kadir, Mohd Zainal Abidin and Mirzaei, Maryam (2014) Modelling and prediction of photovoltaic power output using artificial neural networks. International Journal of Photoenergy, 2014. art. no. 469701. pp. 1-10. ISSN 1110-662X; ESSN: 1687-529X


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.

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Additional Metadata

Item Type: Article
Divisions: Faculty of Engineering
DOI Number: https://doi.org/10.1155/2014/469701
Publisher: Hindawi Publishing Corporation
Keywords: Solar power modelling method; Photovoltaic power output; Artificial neural networks
Depositing User: Nurul Ainie Mokhtar
Date Deposited: 16 Dec 2015 01:28
Last Modified: 21 Nov 2019 06:45
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1155/2014/469701
URI: http://psasir.upm.edu.my/id/eprint/34553
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