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dc.contributor.author
Chin, Jun Xing
dc.contributor.author
Tinoco De Rubira, Tomas
dc.contributor.author
Hug, Gabriela
dc.date.accessioned
2020-10-02T11:21:30Z
dc.date.available
2017-08-15T14:02:26Z
dc.date.available
2017-08-16T15:30:42Z
dc.date.available
2017-12-12T05:09:09Z
dc.date.available
2018-08-13T17:04:02Z
dc.date.available
2018-10-18T05:52:02Z
dc.date.available
2020-10-02T11:21:30Z
dc.date.issued
2017
dc.identifier.isbn
978-1-5090-4237-1
en_US
dc.identifier.isbn
978-1-5090-4238-8
en_US
dc.identifier.other
10.1109/ptc.2017.7981184
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/228766
dc.description.abstract
Smart meter adoption in electricity networks introduces privacy risks for consumers due to the increased measurement frequency and granularity. In particular, consumer behaviour and lifestyle choices may be inferred from their metering data using various Non-Intrusive Load Monitoring techniques. To protect consumer privacy, energy storage controlled by a Model-Distribution Predictive Control scheme can be utilised to mask the actual energy consumption by measuring privacy loss through the mutual information between actual and grid-visible energy consumption. However, the poor scalability of this approach limits the size of the prediction horizon, which is important for both energy cost and privacy loss reduction. In this paper, we propose using time aggregation to increase the reach of the controller's prediction horizon, and describe how to correctly model the statistics in this setting. Results show that with the proposed time aggregation, information leakage and energy costs can be further reduced without increasing the controller's computational requirements.
en_US
dc.language.iso
en
en_US
dc.publisher
IEEE
en_US
dc.subject
consumer privacy
en_US
dc.subject
model-distribution predictive control
en_US
dc.subject
mutual information
en_US
dc.subject
smart meter
en_US
dc.title
Time aggregation for privacy-protecting EMU based on model-distribution predictive control
en_US
dc.type
Conference Paper
dc.date.published
2017-07-20
ethz.book.title
2017 IEEE Manchester PowerTech
en_US
ethz.pages.start
2061
en_US
ethz.pages.end
2066
en_US
ethz.event
12th IEEE PowerTech Conference (PES 2017)
en_US
ethz.event.location
Manchester, UK
en_US
ethz.event.date
June 18-22, 2017
en_US
ethz.grant
Consumer-Centric Privacy in Smart Energy Grids (COPES)
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Piscataway, NJ
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02632 - Inst. f. El. Energieübertragung u. Hoch. / Power Systems and High Voltage Lab.::09481 - Hug, Gabriela / Hug, Gabriela
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02632 - Inst. f. El. Energieübertragung u. Hoch. / Power Systems and High Voltage Lab.::09481 - Hug, Gabriela / Hug, Gabriela
en_US
ethz.grant.agreementno
160845
ethz.grant.fundername
SNF
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.program
ERA-NET + EJP
ethz.date.deposited
2017-08-15T14:02:28Z
ethz.source
FORM
ethz.source
WOS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2018-08-13T17:04:06Z
ethz.rosetta.lastUpdated
2021-02-15T17:47:50Z
ethz.rosetta.versionExported
true
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/177106
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/219930
ethz.COinS
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