Research on prediction of electric power load based on improved ABC and LS-SVM algorithm

LI Wen-jiang; CHEN Yang

doi:10.13873/j.1000-97872013.05.015

Chinese

您当前的位置：

首页 >

文章列表页 >

Research on prediction of electric power load based on improved ABC and LS-SVM algorithm

更新时间：2025-09-11

- Research on prediction of electric power load based on improved ABC and LS-SVM algorithm
- Vol. 32, Issue 5, Pages: 57-59+63(2013)
- 作者机构：
  
  辽宁工程技术大学电气与控制工程学院
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-97872013.05.015
  CLC： TM715
- Published：2013
- 稿件说明：
移动端阅览
[1]李文江,陈阳.基于改进ABC与LS-SVM算法的电力负荷预测的研究[J].传感器与微系统,2013,32(05):57-59+63.

LI Wen-jiang, CHEN Yang. Research on prediction of electric power load based on improved ABC and LS-SVM algorithm[J]. 2013, 32(5): 57-59+63.
[1]李文江,陈阳.基于改进ABC与LS-SVM算法的电力负荷预测的研究[J].传感器与微系统,2013,32(05):57-59+63. DOI： 10.13873/j.1000-97872013.05.015.

LI Wen-jiang, CHEN Yang. Research on prediction of electric power load based on improved ABC and LS-SVM algorithm[J]. 2013, 32(5): 57-59+63. DOI： 10.13873/j.1000-97872013.05.015.

摘要

为了解决电力负荷的非线性等问题和帮助电力企业迅速地制定电力的预计交易量

提出一种建立在最小二乘支持向量机算法基础上的电力负荷预测方法。采用改进的ABC算法优化惩罚因子C和核系数σ

再将最优解赋给LS-SVM用以预测。仿真结果证明:基于改进ABC与LS-SVM算法的电力负荷预测方法具有较高的预测精度

更小的误差

是一种有效的预测方法。

Abstract

In order to solve problems such as nonlinear of power load and help electric power enterprise quickly formulate power expected volume of trade

a kind of method based on least squares support vector machine（LS-SVM） algorithm to forecast electric power load is proposed.Using improved ABC algorithm to optimize penalty factor C and nuclear factor σ

then the optimal solution can be assigned to LS-SVM to predict.The simulation results show that the method based on improved ABC and LS-SVM algorithm to forecast electric power load has high precision

less error

it is a kind of effective prediction method.

关键词

Keywords

references

改进人工蜂群算法及其在反演分析中的应用 [J]. 康飞,李俊杰,许青,张运花. 水电能源科学 . 2009(01)

基于支持向量机的短期负荷预测的方法改进 [J]. 李云飞,黄彦全,蒋功连. 西华大学学报(自然科学版) . 2007(02)

短期负荷预测的支持向量机模型参数优化方法研究 [D]. 霍明. 湖南大学 2009

基于最小二乘支持向量机的短期负荷预测方法及应用研究 [D]. 耿艳. 山东大学 2008

基于支持向量机的电力短期负荷预测研究 [D]. 龚灯才. 河海大学 2006

电力系统负荷预测[M]. 中国电力出版社 , 康重庆, 2007

支持向量机导论[M]. 电子工业出版社 , (英)NelloCristianini,（英）JohnShawe-Taylor著, 2004

Prediction of flashover voltage of insulators using least squares support vector machines [J] . Muhsin Tunay Gencoglu,Murat Uyar. Expert Systems With Applications . 2009 (7)

A comparative study of Artificial Bee Colony algorithm [J] . Dervis Karaboga,Bahriye Akay. Applied Mathematics and Computation . 2009 (1)

A powerful and efficient algorithm for numerical function optimization: artificial bee colony （ABC） algorithm [J] . Dervis Karaboga,Bahriye Basturk. Journal of Global Optimization . 2007 (3)

Least Squares Support Vector Machine Classifiers [J] . J.A.K. Suykens,J. Vandewalle. Neural Processing Letters . 1999 (3)

Views

193

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Error compensation method of photoelectric encoder in extreme environment

Temperature prediction method of greenhouse based on PCA-PSO-LSSVM

Approximate solution of nonlinear ordinary differential equations based on LS-SVM

Monitoring of INS based on chaos PSO algorithm optimization parameters of LS-SVM

Comparative study on three modeling methods for voidage measurement

Related Author

GUO Chenghu

WANG Hui

ZHAO Yan

LIU Na

ZHU Jun-chao

ZHANG Bao-feng

YANG Lei

ZHANG Guo-shan

Related Institution

College of Mechanical Engineering,Liaoning Technical University

Tianjin Binhai Tea Grape Science and Technology Development Co Ltd

Tianjin Key Laboratory of Complex System Control Theory and Application,School of Electrical and Electronic Engineering,Tianjin University of Technology

School of Electrical Engineering ＆ Automation,Tianjin University

School of Engineering,Air Force Engineering University

⁰