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 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.