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

Abstract

2014) Implementation of model predictive control for an HVAC system in a mid-size commercial building, HVAC&R Research, 20:1, 121-135,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-ofconcept 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.

Model-predictive control for non-domestic buildings: a critical review and prospects

“…Such an approach has been successfully demonstrated by, among others, Bengea et al (2014) although again, these authors give little detail on the mapping between input power and zone temperatures. We suggest that much greater attention needs to be paid to the overall system architecture in Figure 1, in particular, i) the identification of any static non-linearities and the ramifications of the Hammerstein model they imply, and ii) whether identification of an overarching non-linear model (and the attendant non-convex optimisation) has greater applicability to a wider range of b u i l d i n g t y p e s .…”

“…The projections for energy saving with MPC also need to be viewed critically. For example, Bengea et al (2014) report some spectacular energy savings (60-85%) in a study on a real building but these authors appear to be comparing between a baseline system where the schedules were ". .…”

“…In fact, a number of researchers have already followed this approach. For example, both Bengea et al (2014) and Y. minimised energy cost (which is some proxy for energy consumption) under dynamic energy-pricing environments, and Sturzenegger et al (2013) minimised a measure monotonically related to non-renewable primary energy.…”

Optimal energy management of a small-size building via hybrid model predictive control

“…In order to obtain a model more suitable for the numerical computations required by the MPC controller, a DHA can be translated into a set of linear integer equalities and inequalities known as Mixed Logical Dynamical (MLD) systems. In order to obtain a description of (8) in MLD form, a vector of continuous auxiliary variables is defined as…”

“…In recent years, MPC has been employed for building heating and cooling systems, based on deterministic and stochastic models in, among others, [3][4][5][6][7][8][9] and [10][11][12], respectively. In some cases, a number of system variables are discrete in nature (e.g., some actuators can only be turned on/off), which requires the use of the so-called hybrid MPC approach [13][14][15][16][17][18].…”