An enhanced differential evolution based grey model for forecasting urban water consumption

被引：0

作者：

Wang, Weiwen ^{[1
]}

Jiang, Junyang ^{[1
]}

Fu, Minglei ^{[1
]}

机构：

[1] Zhejiang Univ Technol, Coll Sci, Hangzhou 310023, Zhejiang, Peoples R China

来源：

2014 33RD CHINESE CONTROL CONFERENCE (CCC) | 2014年

关键词：

grey model; differential evolution algorithm; background value optimization; mean absolute percentage error;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Forecasting water consumption plays a great important role in water resource utilization and management. Grey model (GM) with differential evolution (DE) algorithm has obtained much great success in practical forecasting applications, especially for the forecasting problems with little historical information. In this paper, an enhanced DE based GM which named Step-DE-GM is proposed to forecast urban water consumption. Simulation results show that Step-DE-GM(1,1) can reduce the value of mean absolute percentage error (MAPE) by 0.764% and 0.733% compared with GM(1,1) and DE-GM(1,1), which means Step-DE-GM achieves higher prediction accuracy.

引用

页码：7643 / 7648

页数：6

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