Load forecasting using fixed-size Least Squares Support Vector Machines

被引：0
|
作者：
Espinoza, M [1 ]
Suykens, JAK [1 ]
De Moor, B [1 ]
机构：
[1] Katholieke Univ Leuven, ESAT, SCD, SISTA, B-3001 Louvain, Haverlee, Belgium
来源：
COMPUTATIONAL INTELLIGENCE AND BIOINSPIRED SYSTEMS, PROCEEDINGS | 2005年 / 3512卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Based on the Nystrom approximation and the primal-dual formulation of Least Squares Support Vector Machines (LS-SVM), it becomes possible to apply a nonlinear model to a large scale regression problem. This is done by using a sparse approximation of the nonlinear mapping induced by the kernel matrix, with an active selection of support vectors based on quadratic Renyi entropy criteria. The methodology is applied to the case of load forecasting as an example of a real-life large scale problem in industry, for the case of 24-hours ahead predictions. The results are reported for different number of initial support vectors, which cover between 1% and 4% of the entire sample, with satisfactory results.
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页码：1018 / 1026
页数：9
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