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An autonomous MPC scheme for energy-efficient control of building HVAC systems

Model Predictive Control (MPC) is a promising technique for energy efficient control of Heating, Ventilation, and Air Conditioning (HVAC) systems. However, the need for human involvement limits current MPC strategies from widespread deployment, since (i) model identification algorithms require re-tuning of hyper-parameters, and (ii) optimizers may fail to converge within the available control computation time, or get stuck in a local minimum. In this work we propose an autonomous MPC scheme to overcome these issues. Two major features are embedded in this architecture to enable autonomy: (i) a convex identification algorithm with adaptation to time-varying building dynamics, and (ii) a convex optimizer. The model identification algorithm re-runs periodically so as to handle changes in the building’s dynamics. The estimated model is guaranteed to be stable and has desirable physical properties. The optimizer uses a descent and convergent algorithm, with the underlying optimization problem being feasible and convex. Numerical results show that the proposed convex formulation is more reliable in control computation compared to the nonconvex one, and the proposed autonomous MPC architecture reduces energy consumption significantly over a conventional controller.