Application of Support Vector Regression in Power System Short Term Load Forecasting

被引：2
|
作者：
Jiang, Huilan [1 ]
Yu, Xiaoming [1 ]
Yu, Yaozhou [2 ]
机构：
[1] Tianjin Univ, Minist Educ, Key Lab Power Syst Simulat & Control, Tianjin 300072, Peoples R China
[2] Elect Power Design Inst Tianjin, Tianjin 300200, Peoples R China
来源：
ICNC 2008: FOURTH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, VOL 2, PROCEEDINGS | 2008年
关键词：
D O I：
10.1109/ICNC.2008.768
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper presents a new method-combined use of FCM clustering and support vector regression (SVR) for short term load forecasting in power systems. Using the above advantages of SVR, the complicated nonlinear relationships between some forecasting influence factors and the forecasting load can be regressed Meanwhile, this paper chooses training samples by fuzzy clustering according to similarity degree of the input samples in consideration of the periodic characteristic of load change. The results of the practical applications of the proposed method show the usefulness of this method, both the precision and speed of load forecasting can be improved.
引用
收藏
页码：26 / +
页数：2
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