Implementation of model predictive control for an HVAC system in a mid-size commercial building

Abstract

The article presents field experiment results from the implementation of a model predictive controller which optimizes the operation of a variable volume, dual-duct, multi-zone HVAC unit serving an existing mid-size commercial building. This full-scale proof-of-concept study was used to estimate the benefits of implementing advanced building control technologies during a retrofit. The control approach uses dynamic estimates and predictions of zone loads and temperatures, outdoor weather conditions, and HVAC system models to minimize energy consumption while meeting equipment and thermal comfort constraints. The article describes the on-line implementation of the hierarchical control system, including communication of the optimal control scheme with the building automation system, the controlled set-points and the component-level feedback loops, as well as the measured energy and indoor comfort performance benefits from the demonstration. The building-scale experiments and the receding-horizon control algorithm implementation results are described. Site measurements show this algorithm, when implemented in state-of-the-art direct digital control systems, consistently yields energy savings and reduces peak power while improving the indoor thermal comfort. The demonstration results show energy savings of 20% on average during the transition season, 70% on average during heating season, and 10% or more peak power reduction, all relative to pre-configured, rule-based schedules implemented in the retrofitted direct digital control system.