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Next Generation Machine Learning for Urban Water Management
Author(s) -
Khoi Nguyen,
Rodney A. Stewart,
Hong Zhang,
Damien Giurco,
Michael Blumenstein,
Md Shamsur Rahim
Publication year - 2020
Publication title -
water e-journal
Language(s) - English
Resource type - Journals
ISSN - 2206-1991
DOI - 10.21139/wej.2020.003
Subject(s) - computer science
Determining the end uses of water in residential properties can facilitate a more proactive approach to water literacy, awareness and demand management. This type of information enables the public, government and water businesses to implement more cost-effective and targeted demand management and customer engagement strategies. This study sought to develop a next-generation water management system that combines advanced digital metering technology with machine learning to provide customers and water utilities with a breakthrough in household-scale water management. This breakthrough system (Autoflow) provides a range of functions including autonomous water end-use disaggregation, demand forecasting and customer-specific efficiency recommendations.

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