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Optimization of Daylighting and Ener...
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ProQuest Information and Learning Co.
Optimization of Daylighting and Energy Performance Using Parametric Design, Simulation Modeling, and Genetic Algorithms.
紀錄類型:
書目-語言資料,手稿 : Monograph/item
正題名/作者:
Optimization of Daylighting and Energy Performance Using Parametric Design, Simulation Modeling, and Genetic Algorithms./
作者:
Fang, Yuan.
面頁冊數:
1 online resource (139 pages)
附註:
Source: Dissertation Abstracts International, Volume: 78-10(E), Section: A.
Contained By:
Dissertation Abstracts International78-10A(E).
標題:
Architecture. -
電子資源:
click for full text (PQDT)
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.
- 1 online resource (139 pages)
Source: Dissertation Abstracts International, Volume: 78-10(E), Section: A.
Thesis (Ph.D.)--North Carolina State University, 2017.
Includes bibliographical references
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.
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018
Mode of access: World Wide Web
ISBN: 9781369855944Subjects--Topical Terms:
555123
Architecture.
Index Terms--Genre/Form:
554714
Electronic books.
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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
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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.
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