UC Merced

Heating, ventilation and air conditioning (HVAC) is a mechanical system that provides thermal comfort and accepted indoor air quality often instrumented for large-scale buildings. The HVAC system takes a dominant portion of overall building energy consumption and accounts for 50% of the energy used in the U.S. commercial and residential buildings in 2012. The performance and energy saving of building HVAC systems can be significantly improved by the implementation of better and smarter control strategies. Therefore, it is of great benefits to develop automatic, intelligent, optimal and consistent model and control tools to ensure the normal operations of HVAC systems and increase the building energy efficiency.

Motivated by these goals, this thesis presents a parametric modeling approach and a system-level control design for HVAC systems. For the modeling, we establish dynamical models for return air for the air-handiling-unit (AHU) of HVAC systems. The models include temperature and air flow rate models. These models follow the structure of the finite impulse response (FIR) model, and explicitly include the control variables from the AHU, the dynamical states, and the disturbances. Therefore, it is easy to apply this model to control design. Also, the model is flexible with prediction horizon and control horizon with a stability of the accuracy. In data processing, a convolution based low-pass digital filter, Savitzky-Golay filter is used for data smoothing. As a result, the return air flow rate model becomes more feasible with the smoothed data.

Secondly, this thesis study develops a model preidctive control (MPC) algorithm with the application of the dynamical models for AHU optimization problems. This control strategy optimizes the energy consumption of the AHU, and tracks the set points the room temperatures, supply air flow and return air flow rate of the building. The strategy provides physical-based inherent connection between components in AHU by applying damper positions, supply air temperature and outside air flow rate as manipulated variables. The control inputs are explicitly implemented into both the models and objective functions and the optimization structure is computationally efficient. The optimal results show an energy saving average percentage over and track the supply air flow rate and set point of room temperatures in the building effectively. The thermal load, supply air flow rate set points are calculated from thirty-two VAVs, that ensures the internal cooling demand, the static pressure and the ventilation level of the building.

In this thesis, all the data processing and modeling, model validation and implementation of the control algorithm are based on extensive data measurements collected from an office building on the campus of the University of California, at Merced. The control strategy is implemented into the online building automation system (BAS) of the building and can be easily incorporated with other BAS as well because of the explicit formulation.