Title: Target Tracking for Contextual Bandits: Application to Demand Side Management,
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.