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 [BibTeX] [Marc21]
Multi-agent reinforcement learning for adaptive demand response in smart cities
Type of publication: Conference paper
Citation: Vazquez-Canteli_CISBAT2019_2019
Publication status: Published
Booktitle: Journal of Physics: Conference Series
Volume: 1343
Year: 2019
Month: November
Publisher: IOP Publishing Ltd
DOI: 10.1088/1742-6596/1343/1/012058
Abstract: Buildings account for over 70% of the electricity use in the US. As cities grow, high peaks of electricity consumption are becoming more frequent, which leads to higher prices for electricity. Demand response is the coordination of electrical loads such that they react to price signals and coordinate with each other to shave the peaks of electricity consumption. We explore the use of multi-agent deep deterministic policy gradient (DDPG), an adaptive and model-free reinforcement learning control algorithm, for coordination of several buildings in a demand response scenario. We conduct our experiment in a simulated environment with 10 buildings.
Projects Idiap
Authors Vázquez-Canteli, José
Detjeen, Thomas
Henze, Gregor
Kämpf, Jérôme
Nagy, Zoltán
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