Next Generation Machine Learning for Urban Water Management
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Stewart, Rodney
Zhang, Hong
Giurco, Damien
Blumenstein, Michael
Rahim, Shamsur
Griffith University Author(s)
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Abstract
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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Water e-Journal
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5
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1
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© The Author(s) 2020. The attached file is posted here with permission of the copyright owner(s) for your personal use only. No further distribution permitted.For information about this journal please refer to the journal’s website. The online version of this work is licensed under a Creative Commons License, available at http://creativecommons.org/licenses/by-nc-sa/2.1/au/
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Environmental Sciences
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Nguyen, K; Stewart, R; Zhang, H; Giurco, D; Blumenstein, M; Rahim, S, Next Generation Machine Learning for Urban Water Management, Water e-Journal, 5 (1), pp. 1-7