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Stochastic Optimization of Building ...

Tanner, Ryan Adams.

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Stochastic Optimization of Building Control Systems for Mixed-Mode Buildings.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Stochastic Optimization of Building Control Systems for Mixed-Mode Buildings./
作者:	Tanner, Ryan Adams.
面頁冊數:	222 p.
附註:	Source: Dissertation Abstracts International, Volume: 75-09(E), Section: B.
Contained By:	Dissertation Abstracts International75-09B(E).
標題:	Architectural engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3621422
ISBN:	9781303924750

Stochastic Optimization of Building Control Systems for Mixed-Mode Buildings.
Tanner, Ryan Adams.

Stochastic Optimization of Building Control Systems for Mixed-Mode Buildings. - 222 p.

Source: Dissertation Abstracts International, Volume: 75-09(E), Section: B.

Thesis (Ph.D.)--University of Colorado at Boulder, 2014.

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.

ISBN: 9781303924750Subjects--Topical Terms:

3174102
Architectural engineering.

Stochastic Optimization of Building Control Systems for Mixed-Mode Buildings.
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245 1 0 $a Stochastic Optimization of Building Control Systems for Mixed-Mode Buildings.
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500 $a Source: Dissertation Abstracts International, Volume: 75-09(E), Section: B.
500 $a Adviser: Gregor P. Henze.
502 $a Thesis (Ph.D.)--University of Colorado at Boulder, 2014.
520 $a 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.
520 $a 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.
520 $a 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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