Automated Detection of Electric Vehicles in Hourly Smart Meter Data

Simple item page
dc.contributor.affiliationSINTEF AS
dc.contributor.affiliationSINTEF AS & University of Oslo
dc.contributor.affiliationSINTEF Energy Research AS
dc.contributor.affiliationEidsiva Nett AS
dc.contributor.affiliationEidsiva Energi AS
dc.contributor.authorHoffmann, Volker
dc.contributor.authorFesche, Bjørn Ingeberg
dc.contributor.authorIngebrigtsen, Karoline
dc.contributor.authorChristie, Ingrid Nytun
dc.contributor.authorPunnerud, Morten
dc.contributor.countryNorway
dc.contributor.countryNorway
dc.contributor.countryNorway
dc.contributor.countryNorway
dc.contributor.countryNorway
dc.contributor.detailedauthorHoffmann, Volker, SINTEF AS, Norway
dc.contributor.detailedauthorFesche, Bjørn Ingeberg, SINTEF AS & University of Oslo, Norway
dc.contributor.detailedauthorIngebrigtsen, Karoline, SINTEF Energy Research AS, Norway
dc.contributor.detailedauthorChristie, Ingrid Nytun, Eidsiva Nett AS, Norway
dc.contributor.detailedauthorPunnerud, Morten, Eidsiva Energi AS, Norway
dc.date.accessioned2019-07-24T12:41:27Z
dc.date.available2019-07-24T12:41:27Z
dc.date.conferencedate3-6 June 2019
dc.date.issued2019-06-03
dc.description.abstractAutomated detection of EVs from smart meter data can provide important insights for DSOs about spatiotemporal EV charging patterns. However, smart meters typically provide only hourly measurements of consumption while most load disaggregation techniques require at least minute level data. We use machine and deep learning methods to detect EV signatures in hourly smart meter data. Models are trained and evaluated on labelled data, before being tested on unlabelled field data. While balanced models catch about 75% of EVs at false positive rates of 35%, tuned models detect up to 90% of EVs with 10% false positives. When using models to detect EVs on unlabelled Norwegian smart meter data, detections are in line with EV fractions from the national registry as well as expected spatiotemporal patterns. However, models may be confused by baseline consumption patterns. Collection and inclusion of labelled EVs is therefore the next step.
dc.description.conferencelocationMadrid, Spain
dc.description.conferencenameCIRED 2019
dc.description.openaccessYes
dc.description.peerreviewedYes
dc.description.sessionDistributed energy resources and efficient utilisation of electricity
dc.description.sessionidSession 4
dc.identifier.isbn978-2-9602415-0-1
dc.identifier.issn2032-9644
dc.identifier.urihttps://cired-repository.org/handle/20.500.12455/442
dc.identifier.urihttp://dx.doi.org/10.34890/666
dc.language.isoen
dc.publisherAIM
dc.relation.ispartProc. of the 25th International Conference on Electricity Distribution (CIRED 2019)
dc.relation.ispartofseriesCIRED Conference Proceedings
dc.titleAutomated Detection of Electric Vehicles in Hourly Smart Meter Data
dc.title.number1531
dc.typeConference Proceedings
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
CIRED 2019 - 1531.pdf
Size:
206.75 KB
Format:
Adobe Portable Document Format
Download
Published in
CIRED 2019 Conference