Date of Award

Spring 1-1-2014

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

First Advisor

Gregor P. Henze

Second Advisor

Gail S. Brager

Third Advisor

Balaji Rajagopalan

Fourth Advisor

Michael J. Brandemuehl

Fifth Advisor

Clemens Felsmann

Abstract

Mixed mode (MM) buildings are a subset of low-energy buildings that employ both natural mechanical ventilation, often using manually operable windows for natural ventilation, along with other low-exergy cooling systems such as radiant cooling. This combination of systems has proven difficult to control in practice, in particular due to the potential for occupants to significantly impact building performance. Model predictive control (MPC) and rule extraction are promising methods for optimizing MM building systems in an offline setting, and for generating usable control rules that can be implemented in practice.

Simulation studies were performed to investigate the impact that occupant actions have on mixed mode buildings, and to improve the performance of natural ventilation controls in mixed mode buildings while accounting for uncertain occupant behavior. Results show that accounting for occupant behavior in building simulations provides useful insight into the robustness of different control strategies with respect to the impact of occupant actions. Two approaches to improving natural ventilation controls are applied to a physical building; the first seeks to improve on existing control logic by optimizing setpoints, while the second employs MPC and rule extraction to generate all new control logic. Each approach provides insight into potential flaws in existing logic and suggests revised logic that leads to better performance in the presence of occupant behavior.

In a final study, rule extraction is applied to optimal control datasets for multiple seasons and locations to develop control rules that approximate optimal controller performance. Converting state information to state-change information prior to applying rule extraction is shown to improve the performance of extracted rules, and it is shown that rules generated using data for a single season or location do not transfer well to other seasons or locations.

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