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Contribution Details

Type Conference or Workshop Paper
Scope Discipline-based scholarship
Published in Proceedings Yes
Title Adaptive home heating under weather and price uncertainty using GPs and MDPs
Organization Unit
Authors
  • Michael Shann
  • Sven Seuken
Presentation Type paper
Item Subtype Original Work
Refereed Yes
Status Published in final form
Language
  • English
ISBN 978-1-4503-2738-1
Event Title International Conference on Autonomous Agents and Multiagent Systems (AAMAS)
Event Type conference
Event Location Paris, France
Event Start Date May 5 - 2014
Event End Date May 9 - 2014
Place of Publication Richland, SC, USA
Publisher ACM
Abstract Text We consider the problem of adaptive home heating in the smart grid, assuming that real-time electricity prices are being exposed to end-users with the goal of realizing demand-side management. To lower the burden on the end-users, our goal is the design of a smart thermostat that automatically heats the home, optimally trading o the user’s comfort and cost. This is a challenging problem due to two sources of uncertainty: future weather conditions and future electricity prices. Our main technical contribution is a general technique that uses predictive distributions obtained from Gaussian Process (GP) regressions to compute the state transition probabilities of an MDP, such that the solution to the resulting MDP constitutes a sequentially optimal policy. We apply this general approach to the home-heating problem, where we use the predictive distributions of the GPs for the day-ahead external temperatures and electricity prices. The solution to the home-heating MDP constitutes an optimal heating policy that maximizes the user’s utility given the probability information gathered by the Gaussian process model. Via simulations we show that our MDP-based approach outperforms various benchmarks, especially for cost-sensitive users.
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