Hourly Campus Water Demand Forecasting Using a Hybrid EEMD-Elman Neural Network Model

Abstract

Accurate and reliable water demand forecasting is important for effective and sustainable planning and use of water supply infrastructures. In this paper, a hybrid EEMD-Elman neural network model for hourly campus water demand forecast is proposed, aiming at improving the accuracy and reliability of water demand forecast. The proposed method combines the Elman neural network, EEMD method, and phase space reconstruction method providing favorable dynamic forecast characteristics and improving the forecasting accuracy and reliability. Simulation results show that the proposed model provides a better performance of hourly campus water demand forecast by using the real data of water usage of our campus.