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Proceedings Paper

Regional electricity consumption based on least squares support vector machine
Author(s): Zongwu Wang; Yantao Niu
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Paper Abstract

Least squares support vector machine is presented to predict regional electricity consumption in the paper.Least squares support vector machine is a kind of modified support vector machine, the method can use equality constraints for the error instead of inequality constraints which is used in the support vector machine. A certain regional electricity consumption data from 1999 to 2008 are applied to study the regional electricity consumption prediction performance of LSSVM. The least squares support vector machine prediction model of regional electricity consumption is created and the support vector machine model is applied to compare with the least squares support vector machine model.The comparison of relative error between least squares support vector machine prediction model and support vector machine prediction model is given.The experimental result indicates that the proposed model is accurate to predict the electricity consumption.

Paper Details

Date Published: 13 March 2013
PDF: 4 pages
Proc. SPIE 8784, Fifth International Conference on Machine Vision (ICMV 2012): Algorithms, Pattern Recognition, and Basic Technologies, 87840C (13 March 2013); doi: 10.1117/12.2013681
Show Author Affiliations
Zongwu Wang, North China Electric Power Univ. (China)
Yellow River Conservancy Technical Institute (China)
Yantao Niu, North China Electric Power Univ. (China)


Published in SPIE Proceedings Vol. 8784:
Fifth International Conference on Machine Vision (ICMV 2012): Algorithms, Pattern Recognition, and Basic Technologies
Yulin Wang; Liansheng Tan; Jianhong Zhou, Editor(s)

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