[1]刘雨薇,刘 辉,陈馨凝,等.基于DE-GWO-LSSVM模型的用电负荷预测[J].湖北工业大学学报,2019,34(4):30-34+62.
　LIU Yuwei,LIU Hui,CHEN Xinning,et al.Research on Power Load Forecasting Based on DE-GWO-LSSVM Model[J].,2019,34(4):30-34+62.
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基于DE-GWO-LSSVM模型的用电负荷预测()
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《湖北工业大学学报》[ISSN:1003-4684/CN:42-1752/Z]

卷:
34卷
期数:
2019年第4期
页码:
30-34+62
栏目:
湖北工业大学学报
出版日期:
2019-08-30

文章信息/Info

Title:
Research on Power Load Forecasting Based on DE-GWO-LSSVM Model
文章编号:
1003-4684(2019)04-0030-05
作者:
刘雨薇; 刘 辉; 陈馨凝; 李侯君; 邹琪骁
湖北工业大学电气与电子工程学院, 湖北 武汉 430068
Author(s):
LIU Yuwei; LIU Hui; CHEN Xinning; LI Houjun; ZOU Qixiao
School of Electrical and Electronic Engineering,Hubei Univ. of Tech.,Wuhan 430068,China
关键词:
负荷预测;  灰狼优化算法;  最小二乘向量机
Keywords:
load forecasting;  grey wolf optimization algorithm;  least squares vector machine
分类号:
TM715.1
文献标志码:
A
摘要:
为了提高负荷预测的拟合精度,提出一种基于优化灰狼算法的最小二乘支持向量机负荷预测模型,针对标准灰狼算法精度低、收敛速度慢、易陷入局部最优的缺点,采用差分算法优化标准灰狼算法。利用改进的灰狼算法优化最小二乘支持向量机的两个主要参数,建立功率负荷预测研究模型。通过实例分析获得负荷预测结果,利用三种评价指标对比了四种算法模型。实验表明,改进灰狼算法优化最小二乘支持向量机的改进评价指标数值较低,拟合曲线精度更高。
Abstract:
In order to improve the fitting accuracy of load forecasting, a least squares support vector machine load forecasting model based on optimized grey wolf algorithm is proposed. The proposed model is aimed at the shortcomings of standard gray wolf algorithm, such as low precision, slow convergence speed and easy falling into Locally optimal conditions. The differential algorithm is used to optimize the standard gray wolf algorithm. The improved grey wolf algorithm is then applied to optimize the two main parameters of the least squares support vector machine, by which the power load prediction research model is established. In this paper, load forecasting results are obtained by case analysis. Three kinds of evaluation models are used to compare the four algorithm models. Experiment results show that the improved evaluation index of the modified gray-horse algorithm optimized least squares support vector machine is lower, and the accuracy of the fitting curve is higher.

参考文献/References:

[1] Chen B J, Chang M W, Member, et al. Load forecasting using support vector machines: a study on EUNITE competition 2001[J]. IEEE Transactions on Power Systems, 2004, 19(4):1821-1830.
[2] abikunda samuel. 基于LS_SVM的电力系统短期负荷预测研究[D].北京:华北电力大学,2014.
[3] 代鑫波,崔勇,周德祥,等.基于主成分与粒子群算法的LS-SVM短期负荷预测[J].电测与仪表,2012,49(6):5-9.
[4] 马玉鑫.基于LPP-LSSVM的微电网超短期负荷预测算法[J].上海电气技术,2019,12(1):42-45,53.
[5] 郝晓弘,刘鹏娟,汪宁渤.混沌优化PSO-LSSVM算法的短期负荷预测[J].兰州理工大学学报,2019,45(1):85-90.
[6] 公政,姜文,王来河,等.基于BA-LSSVM的短期电力负荷预测研究[J].电子质量,2017(3):1-4,9.
[7] Mirjalili S, Mirjalili S M, Lewis A. Grey wolf optimizer[J]. Advances in Engineering Software, 2014, 69:46-61.
[8] El-Fergany A A, Hasanien H M. Single and multi-objective optimal power flow using grey wolf optimizer and differential evolution algorithms[J]. Electric Machines & Power Systems, 2015, 43(13):1548-1559.
[9] Cortes C, Vapnik V. Support-vector networks[J]. Machine Learning, 1995, 20(3):273-297.
[10] Suykens J A K, Vandewalle J. Least squares support vector machine classifiers[J]. Neural Processing Letters, 1999, 9(3):293-300.
[11] Das K R, Das D, Das J. Optimal tuning of PID controller using GWO algorithm for speed control in DC motor[C]// International Conference on Soft Computing Techniques & Implementations, 2015.
[12] Storn R, Price K. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces[J]. Journal of Global Optimization, 1997, 11(4):341-359.
[13] 金星, 邵珠超, 王盛慧. 一种基于差分进化和灰狼算法的混合优化算法[J]. 科学技术与工程, 2017(16):271-274.

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备注/Memo

备注/Memo:
[收稿日期] 2019-04-28
[基金项目] 国家自然科学基金(41601394)
[第一作者] 刘雨薇(1994-), 女,湖北武汉人,湖北工业大学硕士研究生,研究方向为电气工程
[通信作者] 刘 辉(1962-)男,湖北武汉人,理学博士,湖北工业大学教授,研究方向为电气工程
更新日期/Last Update: 2019-08-25