  This paper studies the automated control method for regulating air
conditioner (AC) loads in incentive-based residential demand response (DR). The
critical challenge is that the customer responses to load adjustment are
uncertain and unknown in practice. In this paper, we formulate the AC control
problem in a DR event as a multi-period stochastic optimization that integrates
the indoor thermal dynamics and customer opt-out status transition.
Specifically, machine learning techniques including Gaussian process and
logistic regression are employed to learn the unknown thermal dynamics model
and customer opt-out behavior model, respectively. We consider two typical DR
objectives for AC load control: 1) minimizing the total demand, 2) closely
tracking a regulated power trajectory. Based on the Thompson sampling
framework, we propose an online DR control algorithm to learn customer
behaviors and make real-time AC control schemes. This algorithm considers the
influence of various environmental factors on customer behaviors and is
implemented in a distributed fashion to preserve the privacy of customers.
Numerical simulations demonstrate the control optimality and learning
efficiency of the proposed algorithm.
