An Ensemble Neural Network Model to Forecast Drinking Water Consumption
Publication: Journal of Water Resources Planning and Management
Volume 148, Issue 5
Abstract
A reliable short-term forecasting model is fundamental to managing a water distribution system properly. This study addresses the problem of the efficient development of a deep neural network model for short-term forecasting of water consumption in small-scale water supply systems. These aqueducts experience significant fluctuations in their consumption due to a small number of users, making them a challenging task. To deal with this issue, this study proposes a procedure to develop an ensemble neural network model. To reinforce the ensemble model to successfully deal with the weekly and yearly seasonality which affect these data, two different time-varying correction modules are proposed. To constitute the ensemble model, the simple recurrent neural network, the long short-term memory, the gated recurrent unit, and the feedforward architectures are analyzed in two case studies. The results show that the proposed ensemble model can achieve a robust and reliable prediction for all four of the architectures adopted. In addition, the results highlight that the proposed correction modules can significantly improve the predictions.
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Data Availability Statement
The data used during the study were provided by a third party. Direct requests for these materials may be made to the provider as indicated in the Acknowledgments.
Acknowledgments
This study was partially funded by the project “TESES-Urb–Techno-Economic Methodologies to Investigate Sustainable Energy Scenarios at the Urban Level” of the Free University of Bozen-Bolzano. The authors would like to thank the anonymous reviewers for their valuable contribution. They would also like to thank Novareti S.p.A. for providing the data for this study.
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Journal of Water Resources Planning and Management
Volume 148 • Issue 5 • May 2022
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© 2022 American Society of Civil Engineers.
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Received: Jan 17, 2021
Accepted: Dec 21, 2021
Published online: Mar 10, 2022
Published in print: May 1, 2022
Discussion open until: Aug 10, 2022
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