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Residential Demand Response Using Reinforcement Learning

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4 Author(s)
O'Neill, D. ; Dept. of Electr. Eng., Stanford Univ., Stanford, CA, USA ; Levorato, M. ; Goldsmith, A. ; Mitra, U.

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.

Published in:

Smart Grid Communications (SmartGridComm), 2010 First IEEE International Conference on

Date of Conference:

4-6 Oct. 2010

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