Forecasting of Electricity Load Based on Improved Particle Swarm Optimization and Support Vector Regression Machine

被引：0
|
作者：
Liu, Limei [1 ]
机构：
[1] Shenyang Inst Engn, Dept Basic Educ, Shenyang 110136, Peoples R China
来源：
PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON TEST, MEASUREMENT AND COMPUTATIONAL METHODS (TMCM 2015) | 2015年 / 26卷
关键词：
power system; support vector regression machine; forecasting of electricity load; improved particle swarm optimization; ALGORITHM; SELECTION;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Support vector regression machine is suitable for small sample decision and it is good to data forecasting capabilities. Its nature of learning method is under the condition of limited information to obtain a good ability in data mining. Accurate electricity load forecasting is an important practical value to our lives. This paper presents a new algorithm that is an improved particle swarm optimization algorithm and support vector regression machine that is proposed to predict electricity load. It is of great significance to forecasting electricity load. The algorithm can optimize training parameters of support vector regression machine by improved particle swarm optimization algorithm. The simulation experimental results indicate that the new algorithm made a meaningful exploration on forecasting electricity load.
引用
收藏
页码：130 / 133
页数：4
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