Modelling and Prediction of Photovoltaic Power Output Using Artificial Neural Networks. (6th April 2014)

• Record Type: Journal Article
• Authors: Saberian, Aminmohammad; Hizam, H.; Radzi, M. A. M.; Ab Kadir, M. Z. A.; Mirzaei, Maryam
• Other Names: Mahmoodi Niyaz Mohammad Academic Editor.
• Is Part Of: International journal of photoenergy. Volume 2014(2014)
• Publication Date: 2014-04-06
• Subjects: Photochemistry -- Periodicals; Photobiology -- Periodicals; Chemistry, Physical and theoretical -- Periodicals; Photochimie; Photobiologie; Chimie physique et théorique; Chemistry, Physical and theoretical; Photobiology; Photochemistry; Electronic journals; Periodicals; 541.35
• DOI: 10.1155/2014/469701
• Languages: English
• ISSNs: 1110-662X

Abstract

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.