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

被引：0
|
作者：
Huang, Yuansheng [1 ]
Yuan, Liming [1 ]
机构：
[1] N China Elect Power Univ, Dept Econ & Management, Baoding, Peoples R China
来源：
ADVANCED MANUFACTURING SYSTEMS, PTS 1-3 | 2011年 / 201-203卷
关键词：
Combination Forecasting; Support Vector Machine; Rough Set; Power Load;
D O I：
10.4028/www.scientific.net/AMR.201-203.2481
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
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.
引用
收藏
页码：2481 / 2487
页数：7
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