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Sustainability 2016, 8(12), 1220;

Feasibility Study on Parametric Optimization of Daylighting in Building Shading Design

School of Architecture, Hongik University, 94 Wausan-ro, Mapo-gu, Seoul 02481, Korea
Digit, 12, Dongmak-ro 2-gil, Mapo-gu, Seoul 04071, Korea
School of Architecture, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA
Authors to whom correspondence should be addressed.
Academic Editors: Francisco Lozano and Marc A. Rosen
Received: 1 August 2016 / Revised: 20 October 2016 / Accepted: 18 November 2016 / Published: 24 November 2016
(This article belongs to the Section Sustainable Education and Approaches)
Full-Text   |   PDF [9059 KB, uploaded 24 November 2016]   |  


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. View Full-Text
Keywords: parametric optimization; daylight; genetic algorithm; facade design; computer simulation parametric optimization; daylight; genetic algorithm; facade design; computer simulation

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Lee, K.S.; Han, K.J.; Lee, J.W. Feasibility Study on Parametric Optimization of Daylighting in Building Shading Design. Sustainability 2016, 8, 1220.

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