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

Abstract

An autonomous adaptive MPC architecture is presented for control of heating, ventilation and air condition (HVAC) systems to maintain indoor temperature while reducing energy use. Although equipment use and occupant changes with time, existing MPC methods are not capable of automatically relearning models and computing control decisions reliably for extended periods without intervention from a human expert. We seek to address this weakness. Two major features are embedded in the proposed architecture to enable autonomy: (i) a system identification algorithm from our prior work that periodically re-learns building dynamics and unmeasured internal heat loads from data without requiring re-tuning by experts. The estimated model is guaranteed to be stable and has desirable physical properties irrespective of the data; (ii) an MPC planner with a convex approximation of the original nonconvex problem. The planner uses a descent and convergent method, with the underlying optimization problem being feasible and convex. A year long simulation with a realistic plant shows that both of the features of the proposed architecture-periodic model and disturbance update and convexification of the planning problem-are essential to get the performance improvement over a commonly used baseline controller. Without these features, though MPC can outperform the baseline controller in certain situations, the benefits may not be substantial enough to warrant the investment in MPC.

Research and Application of Predictive Control Method Based on Deep Reinforcement Learning for HVAC Systems

“…MPC, an optimal control strategy for stochastic systems with random constraints, solves this problem by designing offline uncertainty distributions and has been proved successful in recent years [3][4][5][6]. For instance, Zeng and Barooah presented an autonomous adaptive MPC architecture for HVAC with periodical relearning building dynamics to maintain indoor temperatures while reducing energy usage [7]. Asvadi and Momeni built MPC models for each air handling unit in HVAC systems.…”