粒子群优化的灰色模型在中长期负荷预测中的应用  

Particle Swarm Optimization-based Grey Model for Long-term Load Forecasting

作　　者：杨胡萍[1] 毕志鹏[1] 

机构地区：[1]南昌大学信息工程学院,南昌330031

出　　处：《电测与仪表》2011年第2期40-43,63,共5页Electrical Measurement & Instrumentation

摘　　要：针对GM(1,1)模型的局限性及在负荷预测中存在的问题,提出了一种基于粒子群优化的灰色模型。粒子群算法是一种新的全局优化算法,有很强的全局寻优能力,用它来优化灰色模型的背景值及初始值修正值,能较好地提高电力系统中长期负荷预测的精度。在虚拟仪器LabVIEW平台上进行仿真,验证了所提方法的有效性。In order to settle the problems of the GM （ 1,1 ） in load forecasting, a new grey model based on particle swarm optimization（PSO） is proposed. PSO is a novel random optimization method which has extensive capability of global optimization. The accuracy of long - term load forecasting can be improved significantly by optimizing the background value and original condition by PSO. The effectiveness of the proposed model is validated by simulating on the platform of Labview.

关 键 词：负荷预测 粒子群算法 LABVIEW 

分 类 号：TM715[电气工程—电力系统及自动化]