GSTDTAP  > 资源环境科学
DOI10.1029/2019WR024897
Data Mining to Uncover Heterogeneous Water Use Behaviors From Smart Meter Data
Cominola, A.1; Nguyen, K.2,3; Giuliani, M.4; Stewart, R. A.2,3; Maier, H. R.5; Castelletti, A.4
2019-11-19
发表期刊WATER RESOURCES RESEARCH
ISSN0043-1397
EISSN1944-7973
出版年2019
文章类型Article;Early Access
语种英语
国家Germany; Australia; Italy
英文摘要

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.


英文关键词water demand management water end uses segmentation analysis data mining water use behaviors smart meters
领域资源环境
收录类别SCI-E
WOS记录号WOS:000498312100001
WOS关键词RESIDENTIAL WATER ; CONSUMPTION ; DEMAND ; ENERGY ; SEGMENTATION ; CONSERVATION ; MANAGEMENT ; PATTERNS ; SYSTEM ; IDENTIFICATION
WOS类目Environmental Sciences ; Limnology ; Water Resources
WOS研究方向Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources
引用统计
文献类型期刊论文
条目标识符http://119.78.100.173/C666/handle/2XK7JSWQ/223909
专题资源环境科学
作者单位1.Tech Univ Berlin, Einstein Ctr Digital Future, Chair Smart Water Networks, Berlin, Germany;
2.Griffith Univ, Sch Engn & Built Ment, Gold Coast, Australia;
3.Griffith Univ, Cities Res Inst, Gold Coast, Australia;
4.Polecn Milano, Dept Elect Informat & Bioengn, Milan, Italy;
5.Univ Adelaide, Sch Civil Environm & Min Engn, Adelaide, SA, Australia
推荐引用方式
GB/T 7714
Cominola, A.,Nguyen, K.,Giuliani, M.,et al. Data Mining to Uncover Heterogeneous Water Use Behaviors From Smart Meter Data[J]. WATER RESOURCES RESEARCH,2019.
APA Cominola, A.,Nguyen, K.,Giuliani, M.,Stewart, R. A.,Maier, H. R.,&Castelletti, A..(2019).Data Mining to Uncover Heterogeneous Water Use Behaviors From Smart Meter Data.WATER RESOURCES RESEARCH.
MLA Cominola, A.,et al."Data Mining to Uncover Heterogeneous Water Use Behaviors From Smart Meter Data".WATER RESOURCES RESEARCH (2019).
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