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

  • Xiao Deng
  • Shuai Hou
  • Wen-zhu Li
  • Xin Liu
Conference paper
Part of the Environmental Earth Sciences book series (EESCI)

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.

Keywords

Elman neural networks EEMD Phase space reconstruction Water demand forecasting 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Information and Electric EngineeringHebei University of EngineeringHandanChina

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