Kevin J. Kircher

Ozan Baris Mulayim, Elias N. Pergantis, Levi D. Reyes Premer et al.

Advanced control strategies like Model Predictive Control (MPC) offer significant energy savings for HVAC systems but often require substantial engineering effort, limiting scalability. Reinforcement Learning (RL) promises greater automation and adaptability, yet its practical application in real-world residential settings remains largely undemonstrated, facing challenges related to safety, interpretability, and sample efficiency. To investigate these practical issues, we performed a direct comparison of an MPC and a model-based RL controller, with each controller deployed for a one-month period in an occupied house with a heat pump system in West Lafayette, Indiana. This investigation aimed to explore scalability of the chosen RL and MPC implementations while ensuring safety and comparability. The advanced controllers were evaluated against each other and against the existing controller. RL achieved substantial energy savings (22\% relative to the existing controller), slightly exceeding MPC's savings (20\%), albeit with modestly higher occupant discomfort. However, when energy savings were normalized for the level of comfort provided, MPC demonstrated superior performance. This study's empirical results show that while RL reduces engineering overhead, it introduces practical trade-offs in model accuracy and operational robustness. The key lessons learned concern the difficulties of safe controller initialization, navigating the mismatch between control actions and their practical implementation, and maintaining the integrity of online learning in a live environment. These insights pinpoint the essential research directions needed to advance RL from a promising concept to a truly scalable HVAC control solution.

Kevin J. Kircher, K. Max Zhang

We consider the problem of finite-horizon optimal control design under uncertainty for imperfectly observed discrete-time systems with convex costs and constraints. It is known that this problem can be cast as an infinite-dimensional convex program when the dynamics and measurements are linear, uncertainty is additive, and the risks associated with constraint violations and excessive costs are measured in expectation or in the worst case. In this paper, we extend this result to systems with convex or concave dynamics, nonlinear measurements, more general uncertainty structures and other coherent risk measures. In this setting, the optimal control problem can be cast as an infinite-dimensional convex program if (1) the costs, constraints and dynamics satisfy certain monotonicity properties, and (2) the measured outputs can be reversibly `purified' of the influence of the control inputs through Q- or Youla-parameterization. The practical value of this result is that the finite-dimensional subproblems arising in a variety of suboptimal control methods, notably including model predictive control and the Q-design procedure, are also convex for this class of nonlinear systems. Subproblems can therefore be solved to global optimality using convenient modeling software and efficient, reliable solvers. We illustrate these ideas in a numerical example.