A Study of The Weighted Least Squares Support Vector Machine within The Bayesian Evidence Framework

被引：0

作者：

Chen, Ruoxi ^{[1
]}

Wang, Yunliang ^{[2
]}

机构：

[1] Tianjin Univ Technol, Sch Elect Engn, Tianjin 300384, Peoples R China

[2] Tianjin Univ Technol, Tianjin Key Lab Control Theory & Applicat Complic, Tianjin 300384, Peoples R China

来源：

2016 IEEE/CSAA INTERNATIONAL CONFERENCE ON AIRCRAFT UTILITY SYSTEMS (AUS) | 2016年

关键词：

Short-term load forecasting; Weighted least squares support vector machines; Bayesian evidence framework;

D O I：

暂无

中图分类号：

V [航空、航天];

学科分类号：

08 ; 0825 ;

摘要：

Short-term load forecasting is essential in the safely assigning of every electricity production and distribution facility. So a short-term load forecasting model and algorithm based on the weighted least squares support vector machine within the Bayesian evidence framework is proposed.The optimal parameters of models can be found through three-layer :Bayesian evidence inference.To improve the robustness of the model, WLS-SVM regession model with good generalization performance is established by giving a different weight coefficient to each sample error, which further improves the prediction accuracy of the model

引用

页码：616 / 619

页数：4

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