Short-Term Load Combination Forecast Based on Rough Set and Support Vector Machine

Abstract:

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A short-term load combination forecasting model based on rough set and support vector machine was proposed in this paper, firstly build decision table based on historical data, and data mining the data through attribute reduction algorithms, and then use the results of prediction methods to be the input of the SVM, practical load value to be the output, training according to the algorithm of the SVM. the result shows that the SVM combination forecasting model has a better balance fitting and extrapolation,and its prediction accuracy is better than single prediction model.

Info:

Periodical:

Advanced Materials Research (Volumes 201-203)

Edited by:

Daoguo Yang, Tianlong Gu, Huaiying Zhou, Jianmin Zeng and Zhengyi Jiang

Pages:

2481-2487

DOI:

10.4028/www.scientific.net/AMR.201-203.2481

Citation:

Y. S. Huang and L. M. Yuan, "Short-Term Load Combination Forecast Based on Rough Set and Support Vector Machine", Advanced Materials Research, Vols. 201-203, pp. 2481-2487, 2011

Online since:

February 2011

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Price:

$35.00

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