Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-9653
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Main Title: Data Mining to Uncover Heterogeneous Water Use Behaviors From Smart Meter Data
Author(s): Cominola, Andrea
Nguyen, K.
Giuliani, Matteo
Stewart, Rodney A.
Maier, Holger R.
Castelletti, Andrea
Type: Article
Language Code: en
Abstract: Knowledge 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.
URI: https://depositonce.tu-berlin.de/handle/11303/10758
http://dx.doi.org/10.14279/depositonce-9653
Issue Date: 19-Nov-2019
Date Available: 13-Feb-2020
DDC Class: 550 Geowissenschaften
Subject(s): water demand management
water end uses
segmentation analysis
data mining
water use behaviors
smart meters
Sponsor/Funder: TU Berlin, Open-Access-Mittel - 2019
License: https://creativecommons.org/licenses/by/4.0/
Journal Title: Water Resources Research
Publisher: Wiley ; American Geophysical Union (AGU)
Publisher Place: New York; Washington, DC
Volume: 55
Issue: 11
Publisher DOI: 10.1029/2019WR024897
Page Start: 9315
Page End: 9333
EISSN: 1944-7973
ISSN: 0043-1397
Appears in Collections:FG Fluidsystemdynamik - Strömungstechnik in Maschinen und Anlagen » Publications

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