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Article

An Innovative Hourly Water Demand Forecasting Preprocessing Framework with Local Outlier Correction and Adaptive Decomposition Techniques

1
School of Environment, Harbin Institute of Technology, Harbin 150090, China
2
Shenzhen ANSO Measurement & Control Instruments Co., Ltd., Baoan District, Shenzhen 518000, China
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Authors to whom correspondence should be addressed.
Academic Editor: Kenneth M. Persson
Water 2021, 13(5), 582; https://doi.org/10.3390/w13050582
Received: 26 December 2020 / Revised: 17 February 2021 / Accepted: 19 February 2021 / Published: 24 February 2021
(This article belongs to the Section Urban Water Management)
Accurate forecasting of hourly water demand is essential for effective and sustainable operation, and the cost-effective management of water distribution networks. Unlike monthly or yearly water demand, hourly water demand has more fluctuations and is easily affected by short-term abnormal events. An effective preprocessing method is needed to capture the hourly water demand patterns and eliminate the interference of abnormal data. In this study, an innovative preprocessing framework, including a novel local outlier detection and correction method Isolation Forest (IF), an adaptive signal decomposition technique Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and basic forecasting models have been developed. In order to compare a promising deep learning method Gated Recurrent Unit (GRU) as a basic forecasting model with the conventional forecasting models, Support Vector Regression (SVR) and Artificial Neural Network (ANN) have been used. The results show that the proposed hybrid method can utilize the complementary advantages of the preprocessing methods to improve the accuracy of the forecasting models. The root-mean-square error of the SVR, ANN, and GRU models has been reduced by 57.5%, 27.8%, and 30.0%, respectively. Further, the GRU-based models developed in this study are superior to the other models, and the IF-CEEMDAN-GRU model has the highest accuracy. Hence, it is promising that this preprocessing framework can improve the performance of the water demand forecasting models. View Full-Text
Keywords: hourly water demand; outlier detection and correction; mode decomposition; gated recurrent unit hourly water demand; outlier detection and correction; mode decomposition; gated recurrent unit
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MDPI and ACS Style

Hu, S.; Gao, J.; Zhong, D.; Deng, L.; Ou, C.; Xin, P. An Innovative Hourly Water Demand Forecasting Preprocessing Framework with Local Outlier Correction and Adaptive Decomposition Techniques. Water 2021, 13, 582. https://doi.org/10.3390/w13050582

AMA Style

Hu S, Gao J, Zhong D, Deng L, Ou C, Xin P. An Innovative Hourly Water Demand Forecasting Preprocessing Framework with Local Outlier Correction and Adaptive Decomposition Techniques. Water. 2021; 13(5):582. https://doi.org/10.3390/w13050582

Chicago/Turabian Style

Hu, Shiyuan, Jinliang Gao, Dan Zhong, Liqun Deng, Chenhao Ou, and Ping Xin. 2021. "An Innovative Hourly Water Demand Forecasting Preprocessing Framework with Local Outlier Correction and Adaptive Decomposition Techniques" Water 13, no. 5: 582. https://doi.org/10.3390/w13050582

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