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Building Control

The following is a list of recent building control projects conducted at the Automatic Control Laboratory.

Simulation-based potential assessment of MPC

In a first phase of the OptiControl project, the potential of model predictive control strategies was assessed in simulations. For a large set of building/systems/weather combinations, whole year simulations of industry standard rule-based controller (RBC) and MPC were performed. Validated one-zone bilinear resistance-capacitance type models were used. The simulations showed that (assuming no model-mismatch), MPC can in many cases save significant amounts (~20%) of control energy compared to conventional RBC.

MPC of Buildings
Figure 1: Model predictive building control.

Model predictive control of a Swiss office building

Phase 2 of the OptiControl project provided a proof-of-concept for the integrated control of a whole office building. It addressed problems such as the modeling of real buildings, plant-model mismatch and compatibility with pre-installed control systems. On a typical Swiss office building shown in Figure 2 with a conditioned floor area of ca. 6000 m2, five office floors were controlled for a total period of seven months. The MPC provided integrated control of the TABS, the air handling unit (including energy recovery/heating coil/evaporative cooler), radiators and centrally controlled blinds. MPC was implemented as a high-level controller, sending set-points and operating modes to the existing low-level control. The control algorithm ran in Matlab on a PC, connected through a BACnet-OPC client to the building automation system. Figure 3 shows the spring/summer experimental period. The maximum (over all rooms) measured integrated comfort violations were approximately 10 Kelvin-hours, mostly stemming from the end of June when the cooling system was overwhelmed. Comfort also was maintained in the heating season experiments (not shown). This was underlined by the facility manager’s feedback.

Demonstration building.
Figure 2: Building controlled by MPC. From [1].
MPC room temperature performance
Figure 3: MPC room temperature performance (2012). EN15251 comfort constraints. From [1].

BRCM Matlab Toolbox: Model Generation for Model Predictive Building Control

Creating an accurate building model that is simple enough to allow the resulting MPC problem to be tractable is a crucial task in the control development. The building resistance-capacitance modeling (BRCM) Toolbox provides a means for the fast generation of bilinear resistance-capacitance type models from basic geometry, construction and building systems data. It also supports the generation of the corresponding potentially time-varying costs and constraints. The full building model is constructed in a stepwise procedure: i) Automated generation of the building’s linear thermal model (describing the heat transfer between zones, walls and ceilings) from construction and geometry data; ii) modeling of external heat fluxes (e.g. solar gains, building systems, internal gains etc.) using parameterizable modular sub-models; iii) discretization. Several comparisons with the widely used building simulation software EnergyPlus have shown average model discrepancies of around 0.5K over three days (a typical MPC horizon). Moreover it is possible to construct the thermal model directly from EnergyPlus input data files.

BRCM floor visualization
Figure 3: BRCM Toolbox visualization of a floor. From [2].

Frequency-Domain Identication of a Ventilated Room for MPC

System identification methods have been used to model a ventilated room (with either constant air flow or constant supply temperature). An office type test room was instrumented for experiments and three models for the room were derived: i) an empirical transfer function estimate (ETFE) derived from a pseudo-random binary sequence input signal; ii) an ETFE derived from a relay feedback approach; iii) a model generated with the BRCM Toolbox. Using additional validation data, the different models and approaches were compared in terms of accuracy and efficiency. The effect of air mixing dynamics was demonstrated in a further experiment to be one of the main differences between the experimentally identified and the RC model. An additional pole can be added to the RC model in order to compensate for the differences.

BRCM floor visualization
Figure 4: Schematic of the instrumented room. From [3].
BRCM floor visualization
Figure 5: Transfer function from supply air heating to room air temperature measured by the table sensor. From [3].

[1] Sturzenegger, Gyalistras, Gwerder, Sagerschnig, Morari, Smith. Model Predictive Control of a Swiss Office Building . 11th REHVA World Congress Clima 2013.

[2] Sturzenegger, Gyalistras, Semeraro, Morari, Smith. BRCM Matlab Toolbox: Model Generation for Model Predictive Building Control . American Control Conference 2014

[3] Sturzenegger, Keusch, Muffato, Kunz, Smith. Frequency-Domain Identication of a Ventilated Room for MPC . 19th IFAC World Congress