Inferring Demand in Drinking Water Distribution Systems through Stratified Sampling of Billing Data for Smart Meter Installation
Publication: Journal of Water Resources Planning and Management
Volume 150, Issue 8
Abstract
The importance of urban water supply systems and public services is globally recognized. Nonrevenue water directly affects a water utility’s economic, financial, and environmental sustainability. In Portugal, the mean of the nonrevenue water for the distribution systems corresponded to 28.8% in 2019. Smart metering technology is crucial for consumption monitoring and enhancing apparent and real loss network management (e.g., water meters’ global error evaluation, detection of illegal uses, and real loss estimation through the minimum night flow analysis). However, this technology is expensive in acquisition, installation, operation, and maintenance. This study aims to support water utilities in inferring the total consumption using a representative sample of customers with smart meters instead of smart metering data from all customers. A stratified sampling was considered using only the customers’ billing time series for the strata definition. A predominantly domestic zone was used, and eight strata were obtained with a clustering analysis [temporal correlation (CORT) dissimilarity and Ward method]. Stratified sampling was applied to minimize the variance of the total water consumption estimator. A representative sample of 259 dimensions (53%) was chosen to infer, with small errors, essential consumption statistics for water utilities: total consumption (with an error of 0.12%), total consumption time series, water consumption patterns, minimum night consumption, and volume distribution by the flow rate. The successful outcomes obtained were crucial in supporting the proposed methodology. This study has provided evidence that installing smart meters for all consumers in a distribution network area is not necessary to acquire accurate and meaningful consumption information crucial for effective network management and water loss control. Moreover, using only billing data to perform the sample selection of consumers is useful for water utilities, because they may face difficulties obtaining extra consumer information.
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Data Availability Statement
All data used during the study are confidential in nature, as it relates to the consumption data of private customers and may only be provided in an anonymized form with the authorization of the water utility responsible for its collection.
Acknowledgments
The first author acknowledges the Fundação para a Ciência e Tecnologia (FCT), Portugal, for the Ph.D. Grant No. SFRH/BD/131382/2017. The authors also acknowledge FCT for the projects UID/Multi/04621/2019, DSAIPA/DS/0089/2018, and PTDC/EGE-ECO/30535/2017 of CEMAT/ IST-ID, Center for Computational and Stochastic Mathematics, Instituto Superior Técnico, ULisboa. The authors would also like to give thanks to the water utility, Águas da Figueira, and to Ana Agostinho for helping with the initial data processing and analysis.
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Published In
Journal of Water Resources Planning and Management
Volume 150 • Issue 8 • August 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Sep 22, 2023
Accepted: Mar 1, 2024
Published online: Jun 6, 2024
Published in print: Aug 1, 2024
Discussion open until: Nov 6, 2024
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