Skip to main navigation Skip to search Skip to main content

Residential Demand Response Using Reinforcement Learning

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

Abstract

We present a novel energy management system for residential demand response. The algorithm, named CAES, reduces residential energy costs and smooths energy usage. CAES is an online learning application that implicitly estimates the impact of future energy prices and of consumer decisions on long term costs and schedules residential device usage. CAES models both energy prices and residential device usage as Markov, but does not assume knowledge of the structure or transition probabilities of these Markov chains. CAES learns continuously and adapts to individual consumer preferences and pricing modifications over time. In numerical simulations CAES reduced average end-user financial costs from 16% to 40% with respect to a price-unaware energy allocation.

Original languageEnglish (US)
Title of host publication2010 1st IEEE International Conference on Smart Grid Communications, SmartGridComm 2010
PublisherIEEE Computer Society
Pages409-414
Number of pages6
ISBN (Print)9781424465125
DOIs
StatePublished - 2010
Externally publishedYes
Event1st IEEE International Conference on Smart Grid Communications, SmartGridComm 2010 - Gaithersburg, MD, United States
Duration: Oct 4 2010Oct 6 2010

Publication series

Name2010 1st IEEE International Conference on Smart Grid Communications, SmartGridComm 2010

Conference

Conference1st IEEE International Conference on Smart Grid Communications, SmartGridComm 2010
Country/TerritoryUnited States
CityGaithersburg, MD
Period10/4/1010/6/10

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture
  • Communication

Fingerprint

Dive into the research topics of 'Residential Demand Response Using Reinforcement Learning'. Together they form a unique fingerprint.

Cite this