Title :
An enhanced differential evolution based grey model for forecasting urban water consumption
Author :
Weiwen Wang ; Junyang Jiang ; Minglei Fu
Author_Institution :
Coll. of Sci., Zhejiang Univ. of Technol., Hangzhou, China
Abstract :
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.
Keywords :
evolutionary computation; forecasting theory; grey systems; water resources; enhanced differential evolution based grey model; step-DE-GM(1,1) algorithm; urban water consumption forecasting; water resource management; water resource utilization; Accuracy; Data models; Forecasting; Integrated circuit modeling; Mathematical model; Predictive models; Vectors; background value optimization; differential evolution algorithm; grey model; mean absolute percentage error;
Conference_Titel :
Control Conference (CCC), 2014 33rd Chinese
Conference_Location :
Nanjing
DOI :
10.1109/ChiCC.2014.6896274