The Effectiveness of Feature Selection Method in Solar Power Prediction
Joint Authors
Hossain, Md Rahat
Ali, A. B. M. Shawkat
Oo, Amanullah Maung Than
Source
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-08-06
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Hossain, Md Rahat& Oo, Amanullah Maung Than& Ali, A. B. M. Shawkat. 2013. The Effectiveness of Feature Selection Method in Solar Power Prediction. Journal of Renewable Energy،Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-510967
Modern Language Association (MLA)
Hossain, Md Rahat…[et al.]. The Effectiveness of Feature Selection Method in Solar Power Prediction. Journal of Renewable Energy No. 2013 (2013), pp.1-9.
https://search.emarefa.net/detail/BIM-510967
American Medical Association (AMA)
Hossain, Md Rahat& Oo, Amanullah Maung Than& Ali, A. B. M. Shawkat. The Effectiveness of Feature Selection Method in Solar Power Prediction. Journal of Renewable Energy. 2013. Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-510967
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-510967