Online Learning for Demand Response Conference Paper

abstract

• 2015 IEEE. Demand response is a key component of existing and future grid systems facing increased variability and peak demands. Scaling demand response requires efficiently predicting individual responses for large numbers of consumers while selecting the right ones to signal. This paper proposes a new online learning problem that captures consumer diversity, messaging fatigue and response prediction. We use the framework of multi-armed bandits model to address this problem. This yields simple and easy to implement index based learning algorithms with provable performance guarantees.

name of conference

• 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton)

authors

• Kalathil, Dileep

published proceedings

• 2015 53RD ANNUAL ALLERTON CONFERENCE ON COMMUNICATION, CONTROL, AND COMPUTING (ALLERTON)

author list (cited authors)

• Kalathil, D., & Rajagopal, R.

citation count

• 14

complete list of authors

• Kalathil, Dileep||Rajagopal, Ram

publication date

• January 2015

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• n2474-0195ISSN  Journal

Digital Object Identifier (DOI)

• 10.1109/allerton.2015.7447007

International Standard Book Number (ISBN) 13

• 9781509018239

start page

• 218

end page

• 222

URL

• http://dx.doi.org/10.1109/allerton.2015.7447007