Medium and Long-term Electric Load Forecasting based on Chaos SVM

被引：0

作者：

Wang Deji ^{[1
]}

Lian Jie ^{[2
]}

Xu Bo ^{[3
]}

Ma Yumin ^{[4
]}

Zhang Yanbo ^{[5
]}

机构：

[1] China Natl Tobacco Corp, Staff Dev Inst, Zhengzhou 450008, Peoples R China

[2] Henan Radio & Televis Univ, Zhengzhou 450008, Peoples R China

[3] Petro China Pipeline Res & Dev Ctr, Langfang 065000, Peoples R China

[4] Changqin Subsidiary Oil Gas Pipeline Co, Langfang, Peoples R China

[5] PetroChina Pipeline Co, Prod Dept, Langfang, Peoples R China

来源：

PROCEEDINGS OF THE 10TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA 2012) | 2012年

关键词：

Chaos; Prediction; SVM;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Because traditional prediction algorithm can not accurately forecast long-term electricity load, chaos SVM prediction algorithm was introduced and some of its characteristics were discussed. The kernel function was chosen under the guidance of the geometric information. The experiment shows that the algorithm is more accurate and effective than the others.

引用

页码：660 / 663

页数：4

共 7 条

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Ma Junhai
Liu Zengrong
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[5] JIN Zhong-wei, 2008, SHANGHAI U TECHNOLOG, V30, P129
[6] Kang Chongqing, 2004, Automation of Electric Power Systems, V28, P1
[7] Ma JH, 1999, '99 INTERNATIONAL CONFERENCE ON MANAGEMENT SCIENCE & ENGINEERING, PROCEEDINGS, VOLS 1 AND 2, P106

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