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Water demand estimation and outlier detection from smart meter data using classification and Big Data methods

Author
García, D.; González, D.; Quevedo, J.; Puig, V.; Saludes, J.
Type of activity
Presentation of work at congresses
Name of edition
2nd New Developments in IT & Water Conference
Date of publication
2015
Presentation's date
2015-02-09
Book of congress proceedings
2nd New Developments in IT & Water Conference, 8-10 February 2015, Rotterdam (Holland)
First page
1
Last page
8
Repository
http://hdl.handle.net/2117/26473 Open in new window
Abstract
Automatic Meter Reading (AMR) systems are being deployed in many cities to obtain insight into the status and the behavior of District Metering Area (DMA) with more granularity. Until now, the water consumption readings of the population were taken one per month or one each two-months. In contrast, AMR systems provide hourly readings for households and more frequent readings for big consumers. On the one hand, this paper aims at predicting water demand and detect suspicious behaviors – e.g. a ...
Citation
Garcia, D. [et al.]. Water demand estimation and outlier detection from smart meter data using classification and Big Data methods. A: New Developments in IT & Water. "2nd New Developments in IT & Water Conference, 8-10 February 2015, Rotterdam (Holland)". Rotterdam: 2015, p. 1-8.
Keywords
Smart meters, big data, clustering, water demand
Group of research
CS2AC-UPC - Supervision, Safety and Automatic Control
SAC - Advanced Control Systems
SIC - Smart Control Systems

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