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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 |
|
Presentation Type | paper |
Item Subtype | Original Work |
Refereed | Yes |
Status | Published in final form |
Language |
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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. |
Related URLs | |
Other Identification Number | merlin-id:10436 |
PDF File | Download from ZORA |
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