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Data-Driven Predictive Control Optimization for Natural Ventilation in Buildings.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data-Driven Predictive Control Optimization for Natural Ventilation in Buildings./
作者:	Zhang, Wei.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	384 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
Contained By:	Dissertations Abstracts International83-02B.
標題:	Architectural engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28499333
ISBN:	9798534680881

Data-Driven Predictive Control Optimization for Natural Ventilation in Buildings.
Zhang, Wei.

Data-Driven Predictive Control Optimization for Natural Ventilation in Buildings. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 384 p.

Source: Dissertations Abstracts International, Volume: 83-02, Section: B.

Thesis (Ph.D.)--Harvard University, 2021.

This item is not available from ProQuest Dissertations & Theses.

Natural ventilation reflects an emphasis on self-control in architecture: it is a crucial aim in the creation of healthy, comfortable indoor environments with low energy consumption. The design of naturally ventilated buildings and their control algorithms signifies the extension of design boundaries to stochastic elements in the natural environment. Unlike the heating, ventilation, and air conditioning (HVAC) system, the driven forces of natural ventilation, such as the wind and the temperature difference, are not reinforced by a mechanical fan. In buildings, the only controllable component of natural ventilation is the operable window. Conventional natural ventilation control strategies lack the capacity fully to use weather information (e.g., outdoor temperature, wind direction and speed) or rely on rule-based control. Therefore, such strategies typically do not fully achieve the goal of high energy performance. This research developed a controllable natural ventilation system (CNVS) to co-optimize the occupant's thermal comfort, indoor air quality and building energy efficiency, as well as its supportive Internet of things (IoT) research platform. The seasonal CNVS is a 2-layer modular structure. The three CNVS systems (summer, winter, spring/fall) used a combination of a daytime module, a nighttime module, and an occupancy module. The development of the CNVS framework relied on the data-driven predictive control. The evaluation of CNVS was realized in the lab of Harvard HouseZero building.

ISBN: 9798534680881Subjects--Topical Terms:

3174102
Architectural engineering.
Subjects--Index Terms:

Controllable natural ventilation system

Data-Driven Predictive Control Optimization for Natural Ventilation in Buildings.
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500 $a Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
500 $a Advisor: Malkawi, Ali.
502 $a Thesis (Ph.D.)--Harvard University, 2021.
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520 $a Natural ventilation reflects an emphasis on self-control in architecture: it is a crucial aim in the creation of healthy, comfortable indoor environments with low energy consumption. The design of naturally ventilated buildings and their control algorithms signifies the extension of design boundaries to stochastic elements in the natural environment. Unlike the heating, ventilation, and air conditioning (HVAC) system, the driven forces of natural ventilation, such as the wind and the temperature difference, are not reinforced by a mechanical fan. In buildings, the only controllable component of natural ventilation is the operable window. Conventional natural ventilation control strategies lack the capacity fully to use weather information (e.g., outdoor temperature, wind direction and speed) or rely on rule-based control. Therefore, such strategies typically do not fully achieve the goal of high energy performance. This research developed a controllable natural ventilation system (CNVS) to co-optimize the occupant's thermal comfort, indoor air quality and building energy efficiency, as well as its supportive Internet of things (IoT) research platform. The seasonal CNVS is a 2-layer modular structure. The three CNVS systems (summer, winter, spring/fall) used a combination of a daytime module, a nighttime module, and an occupancy module. The development of the CNVS framework relied on the data-driven predictive control. The evaluation of CNVS was realized in the lab of Harvard HouseZero building.
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