In the era of Smart Cities, in which the paradigms of smart water and smart grid are keywords of technological progress, advancements in metering systems allow for water consumption data collection at the end-use level, which is necessary to profile users' behaviors and to promote sustainable use of water resources. In this paper, a real case study of residential water end-use consumption monitoring shows how data collected at a high-resolution rate allow for the evaluation of consumption profiles. The analysis was carried out by calculating consumption statistics, hourly consumption patterns, daily use frequency, and weekly use frequency. Then, the comparison of two consumption profiles, computed before and after the COVID-19 lockdown, allows us to understand how a change in social and economic factors can affect users' behavior. Finally, new perspectives for water demand modeling and management, based on data with high temporal frequency, are presented.

Impact of COVID-19 emergency on residential water end-use consumption measured with a high-resolution IoT system

Di Mauro, A;Santonastaso, GF;Venticinque, S;Di Nardo, A
2021

Abstract

In the era of Smart Cities, in which the paradigms of smart water and smart grid are keywords of technological progress, advancements in metering systems allow for water consumption data collection at the end-use level, which is necessary to profile users' behaviors and to promote sustainable use of water resources. In this paper, a real case study of residential water end-use consumption monitoring shows how data collected at a high-resolution rate allow for the evaluation of consumption profiles. The analysis was carried out by calculating consumption statistics, hourly consumption patterns, daily use frequency, and weekly use frequency. Then, the comparison of two consumption profiles, computed before and after the COVID-19 lockdown, allows us to understand how a change in social and economic factors can affect users' behavior. Finally, new perspectives for water demand modeling and management, based on data with high temporal frequency, are presented.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11591/463541
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