Paper/Poster/Presentation Title
Residential water demand monitoring at the end-use level: a pilot study site in Naples (Italy)
Keywords
Water end-use consumption; data collection; water demand management
Start Date
15-9-2020 3:40 PM
End Date
15-9-2020 4:00 PM
Abstract
Over the past decades, technological innovation, globalization, and climate change generated an unprecedented interest towards the impact of human behaviour on resources consumption. Behavioural studies have boosted the attention on water usage in domestic environments as a key element of the urban metabolism, demanding for knowledge about the humans drivers of household water usage. Such information is required to design tailored demand management programs. On this purpose, water consumption information at end-use level (e.g., shower, dishwasher, etc.) can contribute to understanding how and when water is used in residential setting, along with direct implications on estimating future demands, detecting system leaks, identifying consumption patterns, improving users’ awareness, and personalizing costumers’ profiles. Yet, despite technological development and advances in metering systems, only few water end-use consumption data are available to train data driven algorithms for identifying end-use water usage from aggregate data observed at household level. This work presents a case study of data collection of water end-use information in a residential apartment located in Naples (Italy). In order to generate a repository of real end-use consumption, a monitoring system based on Internet of Things (IoT) technology was implemented and installed on the fixtures of a single-family apartment used as pilot site. The IoT system, composed by a flow-meter, a micro-controller and a content management system, is able to automatically detect, collect and store high-resolution water end-use data. The application allowed the generation of over 8 months of disaggregated data, observed at the scale of single end-use fixture. The resulting repository will be released as open data for the scientific community. Furthermore, this work describes the features of the dataset, a preliminary analysis of collected data that exhibits valuable information about users’ consumption behaviours, the potentiality of end-use information to profile users, and highlights future directions for disaggregation via data-driven techniques.
Residential water demand monitoring at the end-use level: a pilot study site in Naples (Italy)
Over the past decades, technological innovation, globalization, and climate change generated an unprecedented interest towards the impact of human behaviour on resources consumption. Behavioural studies have boosted the attention on water usage in domestic environments as a key element of the urban metabolism, demanding for knowledge about the humans drivers of household water usage. Such information is required to design tailored demand management programs. On this purpose, water consumption information at end-use level (e.g., shower, dishwasher, etc.) can contribute to understanding how and when water is used in residential setting, along with direct implications on estimating future demands, detecting system leaks, identifying consumption patterns, improving users’ awareness, and personalizing costumers’ profiles. Yet, despite technological development and advances in metering systems, only few water end-use consumption data are available to train data driven algorithms for identifying end-use water usage from aggregate data observed at household level. This work presents a case study of data collection of water end-use information in a residential apartment located in Naples (Italy). In order to generate a repository of real end-use consumption, a monitoring system based on Internet of Things (IoT) technology was implemented and installed on the fixtures of a single-family apartment used as pilot site. The IoT system, composed by a flow-meter, a micro-controller and a content management system, is able to automatically detect, collect and store high-resolution water end-use data. The application allowed the generation of over 8 months of disaggregated data, observed at the scale of single end-use fixture. The resulting repository will be released as open data for the scientific community. Furthermore, this work describes the features of the dataset, a preliminary analysis of collected data that exhibits valuable information about users’ consumption behaviours, the potentiality of end-use information to profile users, and highlights future directions for disaggregation via data-driven techniques.
Stream and Session
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