Modelling and prediction of photovoltaic power output using artificial neural networks

• Main Authors: Saberian, Aminmohammad, Hizam, Hashim, Mohd Radzi, Mohd Amran, Ab Kadir, Mohd Zainal Abidin, Mirzaei, Maryam
• Format: Article
• Language: English
• Published: Hindawi Publishing Corporation 2014
• Online Access: http://psasir.upm.edu.my/id/eprint/34553/1/Modelling%20and%20Prediction%20of%20Photovoltaic%20Power%20Output%20Using.pdf; http://psasir.upm.edu.my/id/eprint/34553/; http://www.hindawi.com/journals/ijp/2014/469701/abs/
• Institution: Universiti Putra Malaysia

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.