The recent deployment of smart metering networks is opening new opportunities for advancing the design of residential water demand management strategies (WDMS) relying on improved understanding of water consumers’ behaviors. Recent applications showed that retrieving information on users’ consumption behaviors, along with their explanatory and/or causal factors, is key to spot potential areas where targeting water saving efforts, and to design user-tailored WDMS. In this study, we explore the potential of ICT-based solutions in supporting the design and implementation of highly customized WDMS. On one side, the collection of consumption data at high spatial and temporal resolutions requires big data analytics and machine learning techniques to extract typical consumption features from the metered population of water users. On the other side, ICT solutions and gamifications can be used as effective means for facilitating both users’ engagement and the collection of socio-psychographic users’ information. This latter allows interpreting and improving the extracted profiles, ultimately supporting the customization of WDMS, such as awareness campaigns or personalized recommendations. Our approach is implemented in the SmartH2O platform and demonstrated in a pilot application in Valencia, Spain. Results show how the analysis of the smart metered consumption data, combined with the information retrieved from an ICT gamified web user portal, successfully identify the typical consumption profiles of the metered users and supports the design of alternative WDMS targeting the different users’ profiles.
ICT Solutions for Highly-Customized Water Demand Management Strategies
GIULIANI, MATTEO;COMINOLA, ANDREA;CASTELLETTI, ANDREA FRANCESCO;FRATERNALI, PIERO;
2016-01-01
Abstract
The recent deployment of smart metering networks is opening new opportunities for advancing the design of residential water demand management strategies (WDMS) relying on improved understanding of water consumers’ behaviors. Recent applications showed that retrieving information on users’ consumption behaviors, along with their explanatory and/or causal factors, is key to spot potential areas where targeting water saving efforts, and to design user-tailored WDMS. In this study, we explore the potential of ICT-based solutions in supporting the design and implementation of highly customized WDMS. On one side, the collection of consumption data at high spatial and temporal resolutions requires big data analytics and machine learning techniques to extract typical consumption features from the metered population of water users. On the other side, ICT solutions and gamifications can be used as effective means for facilitating both users’ engagement and the collection of socio-psychographic users’ information. This latter allows interpreting and improving the extracted profiles, ultimately supporting the customization of WDMS, such as awareness campaigns or personalized recommendations. Our approach is implemented in the SmartH2O platform and demonstrated in a pilot application in Valencia, Spain. Results show how the analysis of the smart metered consumption data, combined with the information retrieved from an ICT gamified web user portal, successfully identify the typical consumption profiles of the metered users and supports the design of alternative WDMS targeting the different users’ profiles.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
