Automated Detection of Electric Vehicles in Hourly Smart Meter Data
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Paper number
1531
Working Group Number
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
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.
Table of content
Keywords
Publisher
AIM
Date
2019-06-03
Published in
Permanent link to this record
https://cired-repository.org/handle/20.500.12455/442
http://dx.doi.org/10.34890/666
http://dx.doi.org/10.34890/666
ISSN
2032-9644
ISBN
978-2-9602415-0-1