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The effectiveness of feature selection method in solar power prediction

Hossain, Md Rahat, Oo, Amanullah Maung Than and Ali, A.B.M. Shawkat 2013, The effectiveness of feature selection method in solar power prediction, Journal of renewable energy, vol. 2013, pp. 1-9, doi: 10.1155/2013/952613.

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Title The effectiveness of feature selection method in solar power prediction
Author(s) Hossain, Md Rahat
Oo, Amanullah Maung Than
Ali, A.B.M. Shawkat
Journal name Journal of renewable energy
Volume number 2013
Article ID 952613
Start page 1
End page 9
Total pages 9
Publisher Hindawi Publishing Corporation
Place of publication New York, N.Y.
Publication date 2013
ISSN 2314-4386
2314-4394
Summary This paper empirically shows that the effect of applying selected feature subsets on machine learning techniques significantly improves the accuracy for solar power prediction. Experiments are performed using five well-known wrapper feature selection methods to obtain the solar power prediction accuracy of machine learning techniques with selected feature subsets. For all the experiments, the machine learning techniques, namely, least median square (LMS), multilayer perceptron (MLP), and support vector machine (SVM), are used. Afterwards, these results are compared with the solar power prediction accuracy of those same machine leaning techniques (i.e., LMS, MLP, and SVM) but without applying feature selection methods (WAFS). Experiments are carried out using reliable and real life historical meteorological data. The comparison between the results clearly shows that LMS, MLP, and SVM provide better prediction accuracy (i.e., reduced MAE and MASE) with selected feature subsets than without selected feature subsets. Experimental results of this paper facilitate to make a concrete verdict that providing more attention and effort towards the feature subset selection aspect (e.g., selected feature subsets on prediction accuracy which is investigated in this paper) can significantly contribute to improve the accuracy of solar power prediction.
Language eng
DOI 10.1155/2013/952613
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
Copyright notice ©2013, The Authors
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30058903

Document type: Journal Article
Collections: School of Engineering
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Created: Mon, 09 Dec 2013, 13:37:29 EST

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.