Automated Detection of Electric Vehicles in Hourly Smart Meter Data

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CIRED 2019 - 1531.pdf (206.75 KB)

Paper number

1531

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Conference name

CIRED 2019

Conference date

3-6 June 2019

Conference location

Madrid, Spain

Peer-reviewed

Yes

Short title

Convener

Authors

Hoffmann, Volker, SINTEF AS, Norway
Fesche, Bjørn Ingeberg, SINTEF AS & University of Oslo, Norway
Ingebrigtsen, Karoline, SINTEF Energy Research AS, Norway
Christie, Ingrid Nytun, Eidsiva Nett AS, Norway
Punnerud, Morten, Eidsiva Energi AS, Norway

Abstract

Automated 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.

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Publisher

AIM

Date

2019-06-03

Published in

CIRED 2019 Conference

Permanent link to this record

https://cired-repository.org/handle/20.500.12455/442
http://dx.doi.org/10.34890/666

ISSN

2032-9644

ISBN

978-2-9602415-0-1