Online learning of electric vehicle consumers' charging behavior with missing data

Nasim Yahya Soltani, Georgios B Giannakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Learning in the presence of missing data is a pervasive problem in statistical data analysis. This paper deals with tracking the dynamic charging behavior of electric vehicle consumers, when some of the consumers' consumption decisions are missing. The problem is then formulated as an online classification task with missing labels. An online algorithm is proposed to jointly impute the missing data while at the same time learn from the complete data using an online convex optimization approach.

Original languageEnglish (US)
Title of host publication2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages243-247
Number of pages5
ISBN (Electronic)9781479970889
DOIs
StatePublished - Feb 5 2014
Event2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014 - Atlanta, United States
Duration: Dec 3 2014Dec 5 2014

Publication series

Name2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014

Other

Other2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014
CountryUnited States
CityAtlanta
Period12/3/1412/5/14

Keywords

  • Conditional random field
  • Misses
  • Online convex optimization
  • Smart grid

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  • Cite this

    Soltani, N. Y., & Giannakis, G. B. (2014). Online learning of electric vehicle consumers' charging behavior with missing data. In 2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014 (pp. 243-247). [7032115] (2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GlobalSIP.2014.7032115