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Abstract This study investigates residential indoor water consumption variability across 39 US cities using data from 26,441 single‐family smart water meters. Employing functional data analysis and mixed‐effects random forest, we identified distinct usage patterns across city clusters, with 13 high and 6 low water‐using cities (all in coastal California) differing significantly from 20 medium water‐using cities. Shower and toilet use were primary drivers of indoor use differences between clusters, influenced by both behavioral and fixture efficiency factors. The presence of appliances, certain household features, and weather also affect indoor water use, with varying influence on indoor water use across clusters. Our findings highlight the effectiveness of state‐level water efficiency interventions and emphasize the importance of considering both behavioral factors and appliance efficiency in conservation strategies, providing valuable insights for targeted water demand management in urban areas.
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Emerging investigator series: disaggregating residential sector high-resolution smart water meter data into appliance end-uses with unsupervised machine learning
The residential sector accounts for a significant amount of water consumption in the United States. Understanding this water consumption behavior provides an opportunity for water savings, which are important for sustaining freshwater resources. In this study, we analyzed 1-second resolution smart water meter data from a 4-person household over one year as a demonstration. We disaggregated the data using derivative signals of the influent water flow rate at the water supply point of the home to identify start and end times of water events. k -means clustering, an unsupervised machine learning method, then categorized these water events based on information collected from the appliance/fixture end uses. The use of unsupervised learning reduces the training data requirements and lowers the barrier of implementation for the model. Using the water use profiles, we determined peak demand times and identified seasonal, weekly, and daily trends. These results provide insight into specific water conservation and efficiency opportunities within the household ( e.g. , reduced shower durations), including the reduction of water consumption during peak demand hours. The widespread implementation of this type of smart water metering and disaggregation system could improve water conservation and efficiency on a larger scale and reduce stress on local infrastructure systems and water resources.
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- Award ID(s):
- 1847404
- PAR ID:
- 10290509
- Date Published:
- Journal Name:
- Environmental Science: Water Research & Technology
- Volume:
- 7
- Issue:
- 3
- ISSN:
- 2053-1400
- Page Range / eLocation ID:
- 487 to 503
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/D0EW00724B
