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

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data-Driven Predictive Control Optimization for Natural Ventilation in Buildings./
Author:	Zhang, Wei.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	384 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
Contained By:	Dissertations Abstracts International83-02B.
Subject:	Architectural engineering. -
Online resource:	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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100 1 $a Zhang, Wei. $3 1043738
245 1 0 $a Data-Driven Predictive Control Optimization for Natural Ventilation in Buildings.
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300 $a 384 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
500 $a Advisor: Malkawi, Ali.
502 $a Thesis (Ph.D.)--Harvard University, 2021.
506 $a This item is not available from ProQuest Dissertations & Theses.
506 $a This item must not be sold to any third party vendors.
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.
590 $a School code: 0084.
650 4 $a Architectural engineering. $3 3174102
650 4 $a Artificial intelligence. $3 516317
650 4 $a Design. $3 518875
650 4 $a Datasets. $3 3541416
650 4 $a Sensors. $3 3549539
653 $a Controllable natural ventilation system
653 $a Indoor air quality
653 $a Internet of things
653 $a Predictive control
653 $a Smart building
653 $a Sustainable architecture
690 $a 0729
690 $a 0462
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710 2 $a Harvard University. $b Architecture, Landscape Architecture and Urban Planning. $3 3685493
773 0 $t Dissertations Abstracts International $g 83-02B.
790 $a 0084
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28499333