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Optimization of Daylighting and Ener...

Fang, Yuan.

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Optimization of Daylighting and Energy Performance Using Parametric Design, Simulation Modeling, and Genetic Algorithms.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Optimization of Daylighting and Energy Performance Using Parametric Design, Simulation Modeling, and Genetic Algorithms./
Author:	Fang, Yuan.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	139 p.
Notes:	Source: Dissertation Abstracts International, Volume: 78-10(E), Section: A.
Contained By:	Dissertation Abstracts International78-10A(E).
Subject:	Architecture. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10610711
ISBN:	9781369855944

Optimization of Daylighting and Energy Performance Using Parametric Design, Simulation Modeling, and Genetic Algorithms.
Fang, Yuan.

Optimization of Daylighting and Energy Performance Using Parametric Design, Simulation Modeling, and Genetic Algorithms. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 139 p.

Source: Dissertation Abstracts International, Volume: 78-10(E), Section: A.

Thesis (Ph.D.)--North Carolina State University, 2017.

With the increasing demand for sustainable design and green buildings, performance is becoming an important driving force behind design decisions. Currently, however, only limited design options have been explored, and there are limited performance evaluation methods available for designers in the early design stages. This research proposes a new building performance optimization process that can help designers evaluate both daylighting and energy performance, generate optimized design options, and understand the relationship between design variables and performance metrics.

ISBN: 9781369855944Subjects--Topical Terms:

523581
Architecture.

Optimization of Daylighting and Energy Performance Using Parametric Design, Simulation Modeling, and Genetic Algorithms.
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100 1 $a Fang, Yuan. $3 1035308
245 1 0 $a Optimization of Daylighting and Energy Performance Using Parametric Design, Simulation Modeling, and Genetic Algorithms.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 139 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-10(E), Section: A.
502 $a Thesis (Ph.D.)--North Carolina State University, 2017.
520 $a With the increasing demand for sustainable design and green buildings, performance is becoming an important driving force behind design decisions. Currently, however, only limited design options have been explored, and there are limited performance evaluation methods available for designers in the early design stages. This research proposes a new building performance optimization process that can help designers evaluate both daylighting and energy performance, generate optimized design options, and understand the relationship between design variables and performance metrics.
520 $a The proposed method of performance optimization utilizes various tools and technologies including parametric design, building simulation modeling, and Genetic Algorithms. In this method, building design alternatives are extensively explored through parametric design. Daylighting and energy modeling and simulation are performed to evaluate building performance. Genetic Algorithms is used to identify design options with optimal energy and daylighting performance. A case study was conducted to test and verify the effectiveness of the optimization process. The geometry of the case study building was optimized through the test in three different climate conditions. Various results were analyzed and potential influence of design decisions in different environments were discussed.
590 $a School code: 0155.
650 4 $a Architecture. $3 523581
650 4 $a Architectural engineering. $3 3174102
690 $a 0729
690 $a 0462
710 2 $a North Carolina State University. $b Design. $3 2093402
773 0 $t Dissertation Abstracts International $g 78-10A(E).
790 $a 0155
791 $a Ph.D.
792 $a 2017
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10610711