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

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 in...

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Bibliographic Details
Published inProceedings of the 33rd Chinese Control Conference pp. 7643 - 7648
Main Authors Weiwen Wang, Junyang Jiang, Minglei Fu
Format Conference Proceeding
LanguageEnglish
Published TCCT, CAA 01.07.2014
Subjects
Accuracy
background value optimization
Data models
differential evolution algorithm
Forecasting
grey model
Integrated circuit modeling
Mathematical model
mean absolute percentage error
Predictive models
Vectors
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Summary: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.
ISSN:2161-2927
DOI:10.1109/ChiCC.2014.6896274