Medium and long term power load forecasting using CPSO-GM model

被引：2

作者：

Pan, Guo ^{[1
,2
]}

Ouyang, Aijia ^{[3
,4
]}

机构：

[1] College of Information Science and Engineering, Hunan University

[2] Logistics Information Dept, Hunan Vocational College of Modern Logistics

[3] College of Computer, Hunan Science and Technology economy trade vocation college, Hengyang 421001, Hunan

[4] School of Information Science and Engineering, Hunan City University, Yiyang

来源：

Journal of Networks | 2014年 / 9卷 / 08期

关键词：

Co-evolution; Grey model; Particle swarm optimization; Power load forecasting;

D O I：

10.4304/jnw.9.8.2121-2128

中图分类号：

学科分类号：

摘要：

To overcome the low precision of the basic grey model (GM) in forecasting power loads of medium and longterm, a co-evolutionary particle swarm optimization (CPSO) Grey Model (CPSO-GM) is proposed in this paper. This is done by employing the CPSO to optimize the parameters of the grey model based on the modified formula of the background value. They conduct the simulation experiments on the power load data of medium and long-term by applying the CPSO-GM. The experimental results show that the proposed algorithm is superior to the three different grey prediction models and better to forecast power load data of medium and long-term. © ACADEMY PUBLISHER.

引用

页码：2121 / 2128

页数：7

共 39 条

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