Distributed Model Predictive Control for Building Energy Systems
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Building heating, ventilation, and air-conditioning systems are comprised of many different pieces of equipment, which are controlled individually and separately. By coordinating the operation of these systems in real-time, significant energy savings are possible. However, the changing nature of building systems require that this coordinated control strategy be flexible and modular, with the ability to adapt to new equipment or changes in building operations. This award supports both fundamental and applied research to create and demonstrate algorithms capable of coordinating building systems to reduce energy usage, while maintaining or improving the comfort of building occupants. Results of these research will directly benefit the United States by reducing building energy costs and environmental impact. The project will also help broaden participation of underrepresented groups in engineering research.The hierarchy of control strategies investigated in this project includes lower level control strategies that compensate for the inherently nonlinear behavior of building energy subsystems, while preserving the intuitive controller design and tuning methods common to current equipment. The research also investigates a supervisory distributed predictive control algorithm that is capable of determining to the system-level energy optimal reference trajectories for lower-level controllers. The distributed controller requires minimal communication between systems, and is modular and adaptable to changes in building equipment or operational conditions. The coordinated controller minimizes energy consumption while maintaining occupant comfort. By including occupant demands in the predictive controllers, the system can also provide feedback to users regarding the energy costs associated with specific behaviors, and help shape occupant behavior.