Design optimization of building geometry and fenestration for daylighting and energy performance

•Building geometry is optimized for energy and daylighting performance.•Daylighting performance increase is 38.7%, 31.6%, and 28.8%.•Energy demand reduction is 20.2%, 18.5%, and 17.9%.•The influence of design variables is evaluated through sensitivity analysis.•The skylight width and length are the...

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Vydáno v:Solar energy Ročník 191; s. 7 - 18
Hlavní autoři: Fang, Yuan, Cho, Soolyeon
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Elsevier Ltd 01.10.2019
Pergamon Press Inc
Témata:
Building
Building design
Business metrics
Case studies
Computer simulation
Daylighting
Decisions
Design optimization
Energy
Genetic algorithms
Geometry
Green buildings
Green development
Office buildings
Optimization
Parametric design
Performance measurement
Sensitivity analysis
Skylights
Solar energy
Sustainable design
Building
Parametric design
Daylighting
Energy
Optimization
Genetic algorithms
ISSN:0038-092X, 1471-1257
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Shrnutí:•Building geometry is optimized for energy and daylighting performance.•Daylighting performance increase is 38.7%, 31.6%, and 28.8%.•Energy demand reduction is 20.2%, 18.5%, and 17.9%.•The influence of design variables is evaluated through sensitivity analysis.•The skylight width and length are the most important variables for all locations. With the increasing demand for sustainable design and green buildings, building performance is having a greater influence on design decisions. Design decisions on building envelop, especially on building geometry, window and skylight size and placement are essential in the early design stage. This research proposes a building performance optimization process that can help designers simultaneously evaluate the daylighting and energy performance of numerous design options and generate optimized design. The proposed method utilizes parametric design, building simulation modeling, and genetic algorithms. A case study of a small office building is conducted to test and verify the effectiveness of the optimization process. The geometry of the case study building is optimized in three different climates, Miami, Atlanta, and Chicago. After the optimization, the daylighting performance metric UDI is increased by 38.7%, 31.6%, and 28.8%, and the energy performance metric EUI is decreased by 20.2%, 18.5%, and 17.9% compared to average performance values. Sensitivity analysis is performed to analyze the relationship between design variables and performance metrics. The skylight width and length are the most important variables for all locations, while the influence of the other variables varies greatly.
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ISSN:0038-092X
1471-1257
DOI:10.1016/j.solener.2019.08.039