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Hybrid prediction method for solar power using different computational intelligence algorithms

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journal contribution
posted on 2013-01-01, 00:00 authored by M Hossain, Aman Maung Than OoAman Maung Than Oo, A Ali
Computational Intelligence (CI) holds the key to the development of smart grid to overcome the challenges of planning and optimization through accurate prediction of Renewable Energy Sources (RES). This paper presents an architectural framework for the construction of hybrid intelligent predictor for solar power. This research investigates the applicabil- ity of heterogeneous regression algorithms for 6 hour ahead solar power availability forecasting using historical data from Rockhampton, Australia. Real life solar radiation data is collected across six years with hourly resolution from 2005 to 2010. We observe that the hybrid prediction method is suitable for a reliable smart grid energy management. Prediction reliability of the proposed hybrid prediction method is carried out in terms of prediction error performance based on statistical and graphical methods. The experimental results show that the proposed hybrid method achieved acceptable prediction accuracy. This potential hybrid model is applicable as a local predictor for any proposed hybrid method in real life application for 6 hours in advance prediction to ensure constant solar power supply in the smart grid operation.

History

Journal

Smart grid and renewable energy

Volume

4

Pagination

76 - 87

Publisher

Scientific Research Publishing

Location

Irvine, Calif.

ISSN

2151-481X

eISSN

2151-4844

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal; C Journal article

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