TY - GEN
T1 - Online learning of electric vehicle consumers' charging behavior with missing data
AU - Soltani, Nasim Yahya
AU - Giannakis, Georgios B
PY - 2014/2/5
Y1 - 2014/2/5
N2 - 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.
AB - 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.
KW - Conditional random field
KW - Misses
KW - Online convex optimization
KW - Smart grid
UR - http://www.scopus.com/inward/record.url?scp=84949928891&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84949928891&partnerID=8YFLogxK
U2 - 10.1109/GlobalSIP.2014.7032115
DO - 10.1109/GlobalSIP.2014.7032115
M3 - Conference contribution
AN - SCOPUS:84949928891
T3 - 2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014
SP - 243
EP - 247
BT - 2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014
Y2 - 3 December 2014 through 5 December 2014
ER -
