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

Nasim Yahya Soltani; Georgios B. Giannakis

doi:10.1109/GlobalSIP.2014.7032115

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

Nasim Yahya Soltani, Georgios B. Giannakis

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	243-247
Number of pages	5
ISBN (Electronic)	9781479970889
DOIs	https://doi.org/10.1109/GlobalSIP.2014.7032115
State	Published - Feb 5 2014
Event	2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014 - Atlanta, United States Duration: Dec 3 2014 → Dec 5 2014

Publication series

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

Other

Other	2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014
Country/Territory	United States
City	Atlanta
Period	12/3/14 → 12/5/14

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

Keywords

Conditional random field
Misses
Online convex optimization
Smart grid

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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). Article 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

Online learning of electric vehicle consumers' charging behavior with missing data. / Soltani, Nasim Yahya; Giannakis, Georgios B.
2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 243-247 7032115 (2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Soltani, NY & Giannakis, GB 2014, Online learning of electric vehicle consumers' charging behavior with missing data. in 2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014., 7032115, 2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014, Institute of Electrical and Electronics Engineers Inc., pp. 243-247, 2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014, Atlanta, United States, 12/3/14. https://doi.org/10.1109/GlobalSIP.2014.7032115

Soltani NY, Giannakis GB. Online learning of electric vehicle consumers' charging behavior with missing data. In 2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 243-247. 7032115. (2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014). doi: 10.1109/GlobalSIP.2014.7032115

Soltani, Nasim Yahya ; Giannakis, Georgios B. / Online learning of electric vehicle consumers' charging behavior with missing data. 2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 243-247 (2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014).

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

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