Data-driven adaptive building thermal controller tuning with constraints: A primal-dual contextual Bayesian optimization approach

We study the problem of tuning the parameters of a room temperature controller to minimize its energy consumption, subject to the constraint that the daily cumulative thermal discomfort of the occupants is below a given threshold. We formulate it as an online constrained black -box optimization problem where, on each day, we observe some relevant environmental context and adaptively select the controller parameters. In this paper, we propose to use a data -driven Primal -Dual Contextual Bayesian Optimization (PDCBO) approach to solve this problem.