Urban population growth, climate and land use change are boosting residential water demand. Developing suitable demand-side management strategies is essential to meet future water demands, pursue water savings, and reduce the costs for water utilities. Yet, the effectiveness of water demand management actions relies on our understanding of water consumers’ behavior. While low spatial and temporal resolution water consumption data, as traditionally gathered for billing purposes, hardly support this understanding, the advent of smart metering technologies allowed for quasi real-time monitoring water consumption at the single household level. This advanced our ability in characterizing consumers’ behavior and designing user-oriented residential water demand management strategies. With this work, we revise consolidated practices, identify emerging trends and highlight the challenges and opportunities for future developments given by the use of smart meters. Furthermore, we present the EU-funded SmartH2O project, which aims at creating an ICT platform able to (i) capture and store quasi real time, high resolution residential water usage data, (ii) infer the main determinants of residential water demand and build customers’ behavioral models and (iii) predict how the customer behavior can be influenced by various water demand management strategies. The project exploits a social computing approach for raising users’ awareness about water consumption and pursuing water savings in the residential sector. Preliminary models and results on users behavior modelling and users segmentation using innovative fully automated algorithms are presented.

Advancing Residential Water Management by Smart Metering and Data Intensive Modeling of Consumers’ Behaviors

COMINOLA, ANDREA;GIULIANI, MATTEO;CASTELLETTI, ANDREA FRANCESCO;
2016

Abstract

Urban population growth, climate and land use change are boosting residential water demand. Developing suitable demand-side management strategies is essential to meet future water demands, pursue water savings, and reduce the costs for water utilities. Yet, the effectiveness of water demand management actions relies on our understanding of water consumers’ behavior. While low spatial and temporal resolution water consumption data, as traditionally gathered for billing purposes, hardly support this understanding, the advent of smart metering technologies allowed for quasi real-time monitoring water consumption at the single household level. This advanced our ability in characterizing consumers’ behavior and designing user-oriented residential water demand management strategies. With this work, we revise consolidated practices, identify emerging trends and highlight the challenges and opportunities for future developments given by the use of smart meters. Furthermore, we present the EU-funded SmartH2O project, which aims at creating an ICT platform able to (i) capture and store quasi real time, high resolution residential water usage data, (ii) infer the main determinants of residential water demand and build customers’ behavioral models and (iii) predict how the customer behavior can be influenced by various water demand management strategies. The project exploits a social computing approach for raising users’ awareness about water consumption and pursuing water savings in the residential sector. Preliminary models and results on users behavior modelling and users segmentation using innovative fully automated algorithms are presented.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1005674
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