Water Distribution Nodal Demand Clustering Based on Network Flow Measurements
Publication: Journal of Water Resources Planning and Management
Volume 147, Issue 12
Abstract
The estimation of nodal water demands for water distribution systems has been extensively researched over the last decades. However, demand estimation performance is dependent on selected nodal demand aggregation and available sensor locations. Despite a variety of water demand clustering approaches already proposed, a comprehensive methodology capable of generating cluster solutions with practical interpretation and maximum accuracy is still lacking. To achieve that goal, the current research presents an innovative clustering methodology based on network flow measurements. The procedure follows two primary steps: (1) determination of optimal flow sensor locations, and (2) an integrated approach for cluster identification, which includes cluster scenario generation, demand estimation, and identification metrics. The effectiveness of the proposed method was tested on a synthetic case study with realistic generated spatial patterns. Results demonstrate that finding a high-quality cluster solution is possible by utilizing (1) additional flow sensors installed according to the proposed V-optimal early split methodology, and (2) cluster selection based upon a likelihood metric. In general, the metrics used for both sensor location and cluster identification were found to be critical to identifying the best set of clusters.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available in a repository or online in accordance with funder data retention policies (https://github.com/Paulo-de-Oliveira/Cluster_Theory).
Acknowledgments
The authors would like to gratefully acknowledge the partial funding support provided by the NSF CBET Directorate, Environmental Engineering Program through Award No. 1511959, and the University of Arizona.
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Published In
Journal of Water Resources Planning and Management
Volume 147 • Issue 12 • December 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Sep 3, 2020
Accepted: Aug 25, 2021
Published online: Oct 8, 2021
Published in print: Dec 1, 2021
Discussion open until: Mar 8, 2022
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