Abstract
AbstractKnowledge on the determinants and patterns of water demand for different consumers supports the design of customized demand management strategies. Smart meters coupled with big data analytics tools create a unique opportunity to support such strategies. Yet, at present, the information content of smart meter data is not fully mined and usually needs to be complemented with water fixture inventory and survey data to achieve detailed customer segmentation based on end use water usage. In this paper, we developed a data‐driven approach that extracts information on heterogeneous water end use routines, main end use components, and temporal characteristics, only via data mining existing smart meter readings at the scale of individual households. We tested our approach on data from 327 households in Australia, each monitored with smart meters logging water use readings every 5 s. As part of the approach, we first disaggregated the household‐level water use time series into different end uses via Autoflow. We then adapted a customer segmentation based on eigenbehavior analysis to discriminate among heterogeneous water end use routines and identify clusters of consumers presenting similar routines. Results revealed three main water end use profile clusters, each characterized by a primary end use: shower, clothes washing, and irrigation. Time‐of‐use and intensity‐of‐use differences exist within each class, as well as different characteristics of regularity and periodicity over time. Our customer segmentation analysis approach provides utilities with a concise snapshot of recurrent water use routines from smart meter data and can be used to support customized demand management strategies.
Highlights
After more than 20 years since the first experimental developments of smart water metering (Mayer et al, 1999), the opportunities offered by digital technologies and augmented data mining capabilities, combined with the threats posed by worsening climatic, infrastructural, and societal challenges, are increasingly pushing the water utility sector to transition to the digital age (Beal & Flynn, 2015; Cheong et al, 2016; Turner & White, 2017)
We demonstrated the capabilities of our customer segmentation analysis working on data from 327 single-family households located in Southeast Queensland (SEQ) and Melbourne (Australia), each monitored with smart meters logging water use data with a sampling frequency interval of 5 s for a time span of about 10 months in 2010
We contributed a data-driven, end use-based, water consumer segmentation analysis that expands the information content of water use data sampled with nonintrusive, single-point, smart meters to answer the above question and uncover heterogeneous water use behaviors
Summary
After more than 20 years since the first experimental developments of smart water metering (Mayer et al, 1999), the opportunities offered by digital technologies and augmented data mining capabilities, combined with the threats posed by worsening climatic, infrastructural, and societal challenges, are increasingly pushing the water utility sector to transition to the digital age (Beal & Flynn, 2015; Cheong et al, 2016; Turner & White, 2017). Mining of enhanced sensor data collected with fine spatiotemporal resolution improves utilities' understanding of the evolving status of their network assets (Sensus, 2012), as well as their ability to design both supply- and demand-side management (DSM) strategies (Cominola et al, 2015; Escriva-Bou et al, 2015; Stewart et al, 2010, 2013) In this context, a wide range of contributions in the scientific and technical literature envisions the digital transformation of water utilities and the adoption of digital technologies for the development of smart water networks (Tsakalides et al, 2018). There is growing evidence about the potential of using smart meter-based feedback
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