含有压缩因子的粒子群优化灰色模型在智能电网中的应用  

Compression Factor with Gray Model of Particle Swarm Optimization and its Application in Smart Grid

作　　者：王晓佳[1] 张宝霆[2] 徐达宇[1] 

机构地区：[1]合肥工业大学过程优化与智能决策教育部重点实验室,安徽合肥230009 [2]安徽医科大学,安徽合肥230032

出　　处：《运筹与管理》2012年第3期114-118,共5页Operations Research and Management Science

基　　金：国家863计划重点项目(2009AA043403);国家自然科学基金项目(70631003,90718037);合肥工业大学基金项目(2010HGXJ0083)

摘　　要：针对智能电网对用电量预测的需求和电力系统的负荷特性,在分析了灰色模型GM(1,1)的局限性以及基本粒子群算法在优化GM(1,1)背景值时所出现的不足的基础上,构建了具有压缩因子K的粒子群算法,以此来改进灰色模型的背景值,提出了含有压缩因子的粒子群优化灰色模型KPSO-GM,并把它用于智能电网中用电量预测。实例证明,该算法具有较高的预测精度,有利于提高智能电网的质量。In connection with the demand of consumption forecasting for smart grid, consider the load characteristics of the power system, this paper analyze the limitation of the GM （ 1,1 ） model and the deficiency of particle swarm optimization algorithm when optimizing the grey background value. In order to improve the structural form of background value, this paper utilizes the KPSO-GM model which accordance with compression factor, and it is also used for electricity consumption prediction. Example shows that this model has higher prediction accuracy and can help to improve the quality of smart grid.

关 键 词：智能电网 KPSO-GM模型 粒子群 用电量预测 

分 类 号：N941.5[自然科学总论—系统科学]