Articles | Volume 10, issue 2
https://doi.org/10.5194/dwes-10-75-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/dwes-10-75-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
18 Aug 2017
Research article |
| 18 Aug 2017
Modeling and clustering water demand patterns from real-world smart meter data
Nicolas Cheifetz
CORRESPONDING AUTHOR
Veolia Eau d'Ile de France, Le Vermont, 28, Boulevard de Pesaro, Nanterre 92751, France
Zineb Noumir
Université Paris-Est, IFSTTAR, COSYS, GRETTIA, Marne-la-Vallée 77447, France
Allou Samé
Université Paris-Est, IFSTTAR, COSYS, GRETTIA, Marne-la-Vallée 77447, France
Anne-Claire Sandraz
Veolia Eau d'Ile de France, Le Vermont, 28, Boulevard de Pesaro, Nanterre 92751, France
Cédric Féliers
Veolia Eau d'Ile de France, Le Vermont, 28, Boulevard de Pesaro, Nanterre 92751, France
Véronique Heim
Syndicat des Eaux d'Ile de France (SEDIF), 120 Boulevard Saint-Germain, Paris 75006, France
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Total article views: 3,013 (including HTML, PDF, and XML)
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Total article views: 3,686 (including HTML, PDF, and XML)
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Total article views: 3,013 (including HTML, PDF, and XML)
Thereof 2,862 with geography defined
and 151 with unknown origin.
Total article views: 673 (including HTML, PDF, and XML)
Thereof 670 with geography defined
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Cited
22 citations as recorded by crossref.
- Machine Learning and Data Analytic Techniques in Digital Water Metering: A Review M. Rahim et al. 10.3390/w12010294
- Disaggregation of total energy use into space heating and domestic hot water: A city-scale suited approach M. Schaffer et al. 10.1016/j.energy.2024.130351
- Effects of the COVID-19 Lockdown on Water Consumptions: Northern Italy Case Study S. Alvisi et al. 10.1061/(ASCE)WR.1943-5452.0001481
- Water Consumption Pattern Analysis Using Biclustering: When, Why and How M. Silva et al. 10.3390/w14121954
- A clustering solution for analyzing residential water consumption patterns M. Rahim et al. 10.1016/j.knosys.2021.107522
- Functional data clustering via information maximization X. Li et al. 10.1080/00949655.2023.2215371
- Machine learning for smart water distribution systems: exploring applications, challenges and future perspectives R. Taloma et al. 10.1007/s10462-024-11093-7
- Generating zonal hourly water consumption patterns in water distribution networks using end-use data R. Mazaheri et al. 10.1080/1573062X.2024.2369837
- Pattern Detection and Scaling Laws of Daily Water Demand by SOM: an Application to the WDN of Naples, Italy R. Padulano & G. Del Giudice 10.1007/s11269-018-2140-0
- A cluster analysis approach to sampling domestic properties for sensor deployment T. Menneer et al. 10.1016/j.buildenv.2023.110032
- Effectiveness of smart meter-based urban water loss assessment in a real network with synchronous and incomplete readings C. Bragalli et al. 10.1016/j.envsoft.2018.10.010
- Unsupervised Bayesian Nonparametric Approach with Incremental Similarity Tracking of Unlabeled Water Demand Time Series for Anomaly Detection T. Chan & C. Chin 10.3390/w11102066
- Dynamic Time Warping Clustering to Discover Socioeconomic Characteristics in Smart Water Meter Data D. Steffelbauer et al. 10.1061/(ASCE)WR.1943-5452.0001360
- Uncovering urban water consumption patterns through time series clustering and entropy analysis R. Wang et al. 10.1016/j.watres.2024.122085
- On the importance of similarity characteristics of curve clustering and its applications A. Cheam & M. Fredette 10.1016/j.patrec.2020.04.024
- Exploiting high-resolution data to investigate the characteristics of water consumption at the end-use level: A Dutch case study F. Mazzoni et al. 10.1016/j.wri.2022.100198
- Performance Assessment for Short-Term Water Demand Forecasting Models on Distinctive Water Uses in Korea K. Koo et al. 10.3390/su13116056
- Interval-valued functional clustering based on the Wasserstein distance with application to stock data L. Sun et al. 10.1016/j.ins.2022.05.112
- Contextualising household water consumption patterns in England: A socio-economic and socio-demographic narrative H. Abu-Bakar et al. 10.1016/j.clrc.2023.100104
- Advanced household profiling using digital water meters M. Rahim et al. 10.1016/j.jenvman.2021.112377
- Emerging investigator series: disaggregating residential sector high-resolution smart water meter data into appliance end-uses with unsupervised machine learning G. Bethke et al. 10.1039/D0EW00724B
- Enhancing the accuracy and reliability of non-online metered demand allocation in water distribution systems through dynamic spatiotemporal population distribution Q. Zhang et al. 10.1016/j.watres.2025.123235
22 citations as recorded by crossref.
- Machine Learning and Data Analytic Techniques in Digital Water Metering: A Review M. Rahim et al. 10.3390/w12010294
- Disaggregation of total energy use into space heating and domestic hot water: A city-scale suited approach M. Schaffer et al. 10.1016/j.energy.2024.130351
- Effects of the COVID-19 Lockdown on Water Consumptions: Northern Italy Case Study S. Alvisi et al. 10.1061/(ASCE)WR.1943-5452.0001481
- Water Consumption Pattern Analysis Using Biclustering: When, Why and How M. Silva et al. 10.3390/w14121954
- A clustering solution for analyzing residential water consumption patterns M. Rahim et al. 10.1016/j.knosys.2021.107522
- Functional data clustering via information maximization X. Li et al. 10.1080/00949655.2023.2215371
- Machine learning for smart water distribution systems: exploring applications, challenges and future perspectives R. Taloma et al. 10.1007/s10462-024-11093-7
- Generating zonal hourly water consumption patterns in water distribution networks using end-use data R. Mazaheri et al. 10.1080/1573062X.2024.2369837
- Pattern Detection and Scaling Laws of Daily Water Demand by SOM: an Application to the WDN of Naples, Italy R. Padulano & G. Del Giudice 10.1007/s11269-018-2140-0
- A cluster analysis approach to sampling domestic properties for sensor deployment T. Menneer et al. 10.1016/j.buildenv.2023.110032
- Effectiveness of smart meter-based urban water loss assessment in a real network with synchronous and incomplete readings C. Bragalli et al. 10.1016/j.envsoft.2018.10.010
- Unsupervised Bayesian Nonparametric Approach with Incremental Similarity Tracking of Unlabeled Water Demand Time Series for Anomaly Detection T. Chan & C. Chin 10.3390/w11102066
- Dynamic Time Warping Clustering to Discover Socioeconomic Characteristics in Smart Water Meter Data D. Steffelbauer et al. 10.1061/(ASCE)WR.1943-5452.0001360
- Uncovering urban water consumption patterns through time series clustering and entropy analysis R. Wang et al. 10.1016/j.watres.2024.122085
- On the importance of similarity characteristics of curve clustering and its applications A. Cheam & M. Fredette 10.1016/j.patrec.2020.04.024
- Exploiting high-resolution data to investigate the characteristics of water consumption at the end-use level: A Dutch case study F. Mazzoni et al. 10.1016/j.wri.2022.100198
- Performance Assessment for Short-Term Water Demand Forecasting Models on Distinctive Water Uses in Korea K. Koo et al. 10.3390/su13116056
- Interval-valued functional clustering based on the Wasserstein distance with application to stock data L. Sun et al. 10.1016/j.ins.2022.05.112
- Contextualising household water consumption patterns in England: A socio-economic and socio-demographic narrative H. Abu-Bakar et al. 10.1016/j.clrc.2023.100104
- Advanced household profiling using digital water meters M. Rahim et al. 10.1016/j.jenvman.2021.112377
- Emerging investigator series: disaggregating residential sector high-resolution smart water meter data into appliance end-uses with unsupervised machine learning G. Bethke et al. 10.1039/D0EW00724B
- Enhancing the accuracy and reliability of non-online metered demand allocation in water distribution systems through dynamic spatiotemporal population distribution Q. Zhang et al. 10.1016/j.watres.2025.123235
Latest update: 16 Sep 2025
Short summary
This paper aims at a better understanding of water consumption usage.
This paper aims at a better understanding of water consumption usage.
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