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Hybrid prediction method for solar power using different computational intelligence algorithms

Hossain, Md Rahat, Oo, Amanullah Maung Than and Ali, A.B.M. Shawkat 2013, Hybrid prediction method for solar power using different computational intelligence algorithms, Smart grid and renewable energy, vol. 4, pp. 76-87, doi: 10.4236/sgre.2013.41011.

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Title Hybrid prediction method for solar power using different computational intelligence algorithms
Author(s) Hossain, Md Rahat
Oo, Amanullah Maung ThanORCID iD for Oo, Amanullah Maung Than orcid.org/0000-0002-6914-2272
Ali, A.B.M. Shawkat
Journal name Smart grid and renewable energy
Volume number 4
Start page 76
End page 87
Total pages 12
Publisher Scientific Research Publishing
Place of publication Irvine, Calif.
Publication date 2013
ISSN 2151-481X
2151-4844
Keyword(s) computational intelligence
Heterogeneous Regressions Algorithms
performance evaluation
hybrid method
Mean Absolute Scaled Error (MASE)
Summary 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.
Language eng
DOI 10.4236/sgre.2013.41011
Field of Research 090608 Renewable Power and Energy Systems Engineering (excl Solar Cells)
080109 Pattern Recognition and Data Mining
Socio Economic Objective 850504 Solar-Photovoltaic Energy
HERDC Research category C1.1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30058902

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.