File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: Classification of electric vehicle charging time series with selective clustering

TitleClassification of electric vehicle charging time series with selective clustering
Authors
KeywordsTime series clustering
EV charging curves
Issue Date2020
PublisherElsevier SA. The Journal's web site is located at http://www.elsevier.com/locate/epsr
Citation
Proceedings of the 21st Power Systems Computation Conference (PSCC 2020), Porto, Portugal (virtual conference), 29 June - 3 July 2020. In Electric Power Systems Research, 2020, v. 189, article no. 106695 How to Cite?
AbstractWe develop a novel iterative clustering method for classifying time series of EV charging rates based on their 'tail features'. Our method first extracts tails from a diversity of charging time series that have different lengths, contain missing data, and are distorted by scheduling algorithms and measurement noise. The charging tails are then clustered into a small number of types whose representatives are then used to improve tail extraction. This process iterates until it converges. We apply our method to ACN-Data, a fine-grained EV charging dataset recently made publicly available, to illustrate its effectiveness and potential applications.
Persistent Identifierhttp://hdl.handle.net/10722/288227
ISSN
2023 Impact Factor: 3.3
2023 SCImago Journal Rankings: 1.029
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorSun, C-
dc.contributor.authorLi, T-
dc.contributor.authorLow, S-
dc.contributor.authorLi, VOK-
dc.date.accessioned2020-10-05T12:09:45Z-
dc.date.available2020-10-05T12:09:45Z-
dc.date.issued2020-
dc.identifier.citationProceedings of the 21st Power Systems Computation Conference (PSCC 2020), Porto, Portugal (virtual conference), 29 June - 3 July 2020. In Electric Power Systems Research, 2020, v. 189, article no. 106695-
dc.identifier.issn0378-7796-
dc.identifier.urihttp://hdl.handle.net/10722/288227-
dc.description.abstractWe develop a novel iterative clustering method for classifying time series of EV charging rates based on their 'tail features'. Our method first extracts tails from a diversity of charging time series that have different lengths, contain missing data, and are distorted by scheduling algorithms and measurement noise. The charging tails are then clustered into a small number of types whose representatives are then used to improve tail extraction. This process iterates until it converges. We apply our method to ACN-Data, a fine-grained EV charging dataset recently made publicly available, to illustrate its effectiveness and potential applications.-
dc.languageeng-
dc.publisherElsevier SA. The Journal's web site is located at http://www.elsevier.com/locate/epsr-
dc.relation.ispartofElectric Power Systems Research-
dc.relation.ispartofProceedings of the 21st Power Systems Computation Conference (PSCC 2020)-
dc.subjectTime series clustering-
dc.subjectEV charging curves-
dc.titleClassification of electric vehicle charging time series with selective clustering-
dc.typeConference_Paper-
dc.identifier.emailLi, VOK: vli@eee.hku.hk-
dc.identifier.authorityLi, VOK=rp00150-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.epsr.2020.106695-
dc.identifier.scopuseid_2-s2.0-85089075154-
dc.identifier.hkuros315141-
dc.identifier.volume189-
dc.identifier.spagearticle no. 106695-
dc.identifier.epagearticle no. 106695-
dc.identifier.isiWOS:000594663100006-
dc.publisher.placeSwitzerland-
dc.identifier.issnl0378-7796-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats