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2019 | OriginalPaper | Chapter

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

Authors : Xiao Deng, Shuai Hou, Wen-zhu Li, Xin Liu

Published in: Sustainable Development of Water Resources and Hydraulic Engineering in China

Publisher: Springer International Publishing

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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.

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Title: Hourly Campus Water Demand Forecasting Using a Hybrid EEMD-Elman Neural Network Model
Authors: Xiao Deng
Shuai Hou
Wen-zhu Li
Xin Liu
Publisher: Springer International Publishing
Book: Sustainable Development of Water Resources and Hydraulic Engineering in China
Print ISBN: 978-3-319-61629-2

Electronic ISBN: 978-3-319-61630-8

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-319-61630-8_7