Target Tracking for Contextual Bandits: Application to Demand Side Management

Margaux Brégère 1, 2, 3 Pierre Gaillard 3 Yannig Goude 1 Gilles Stoltz 2
3 SIERRA - Statistical Machine Learning and Parsimony
DI-ENS - Département d'informatique de l'École normale supérieure, CNRS - Centre National de la Recherche Scientifique, Inria de Paris
Abstract : We propose a contextual-bandit approach for demand side management by offering price incentives. More precisely, a target mean consumption is set at each round and the mean consumption is modeled as a complex function of the distribution of prices sent and of some contextual variables such as the temperature, weather, and so on. The performance of our strategies is measured in quadratic losses through a regret criterion. We offer $\sqrt{T}$ upper bounds on this regret (up to poly-logarithmic terms), for strategies inspired by standard strategies for contextual bandits (like LinUCB, Li et al., 2010). Simulations on a real data set gathered by UK Power Networks, in which price incentives were offered, show that our strategies are effective and may indeed manage demand response by suitably picking the price levels.
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Contributor : Gilles Stoltz <>
Submitted on : Friday, January 25, 2019 - 12:08:54 PM
Last modification on : Wednesday, January 30, 2019 - 1:26:20 AM


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  • HAL Id : hal-01994144, version 1
  • ARXIV : 1901.09532



Margaux Brégère, Pierre Gaillard, Yannig Goude, Gilles Stoltz. Target Tracking for Contextual Bandits: Application to Demand Side Management. 2019. ⟨hal-01994144⟩



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