Feasibility Study on Parametric Optimization of Daylighting in Building Shading Design

Kyung Sun Lee, Ki Jun Han and Jae Wook Lee
Additional contact information
Kyung Sun Lee: School of Architecture, Hongik University, 94 Wausan-ro, Mapo-gu, Seoul 02481, Korea
Ki Jun Han: Digit, 12, Dongmak-ro 2-gil, Mapo-gu, Seoul 04071, Korea
Jae Wook Lee: School of Architecture, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA

Sustainability, 2016, vol. 8, issue 12, 1-16

Abstract: Shading design to optimize daylighting is in many cases achieved through a designer’s sense based on prior knowledge and experience. However, computer-assisted parametric techniques can be utilized for daylighting design in an easy and much more accurate way. If such tools are utilized in the early stages of a project, this can be more effective for sustainable design. This study compares the conventional approach, which depends on a designer’s sense of judgment to create optimal indoor lighting conditions by adjusting louver shapes and window patterns, with the approach of making use of genetic algorithms. Ultimately, this study discusses the advantages and disadvantages of those two approaches. As a starting point, 30 designers were instructed to design a facade by manually adjusting several input parameters of shading. The parameters govern six kinds of louver and window types, with the ratio of analysis grid surface area achieving a daylight factor of 2%–5%. Secondly, input parameters were automatically created by using genetic algorithm optimization methods to find optimal fitness data. As a conclusion, conventional approaches result in a strong disposition toward designing certain shading types represented by linear relationships. Computer-assisted daylight simulation can help influence this, being effective when dealing with a large amount of data and non-linear relationships.

Keywords: parametric optimization; daylight; genetic algorithm; facade design; computer simulation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
https://www.mdpi.com/2071-1050/8/12/1220/pdf (application/pdf)
https://www.mdpi.com/2071-1050/8/12/1220/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:8:y:2016:i:12:p:1220-:d:83656

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().