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JACIII Vol.14 No.1 pp. 69-75
doi: 10.20965/jaciii.2010.p0069
(2010)

Paper:

Neural Network Ensemble-Based Solar Power Generation Short-Term Forecasting

Aymen Chaouachi, Rashad M. Kamel, and Ken Nagasaka

Department of Electronic and Information Engineering, Tokyo University of Agriculture and Technology, Nagasaka Laboratory, 2-24-16, Nakamachi, Koganei, Tokyo 184-8588, Japan

Received:
April 6, 2009
Accepted:
August 4, 2009
Published:
January 20, 2010
Keywords:
neural networks, solar power generation, short-term forecasting, neural network ensemble
Abstract

This paper presents the applicability of artificial neural networks for 24 hour ahead solar power generation forecasting of a 20 kW photovoltaic system, the developed forecasting is suitable for a reliable Microgrid energy management. In total four neural networks were proposed, namely: multi-layred perceptron, radial basis function, recurrent and a neural network ensemble consisting in ensemble of bagged networks. Forecasting reliability of the proposed neural networks was carried out in terms forecasting error performance basing on statistical and graphical methods. The experimental results showed that all the proposed networks achieved an acceptable forecasting accuracy. In term of comparison the neural network ensemble gives the highest precision forecasting comparing to the conventional networks. In fact, each network of the ensemble over-fits to some extent and leads to a diversity which enhances the noise tolerance and the forecasting generalization performance comparing to the conventional networks.

Cite this article as:
Aymen Chaouachi, Rashad M. Kamel, and Ken Nagasaka, “Neural Network Ensemble-Based Solar Power Generation Short-Term Forecasting,” J. Adv. Comput. Intell. Intell. Inform., Vol.14, No.1, pp. 69-75, 2010.
Data files:
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