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Feasibility Study on Parametric Optimization of Daylighting in Building Shading Design

Author

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  • 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)

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.

Suggested Citation

  • Kyung Sun Lee & Ki Jun Han & Jae Wook Lee, 2016. "Feasibility Study on Parametric Optimization of Daylighting in Building Shading Design," Sustainability, MDPI, vol. 8(12), pages 1-16, November.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:12:p:1220-:d:83656
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    References listed on IDEAS

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    Cited by:

    1. Fabrizio M. Amoruso & Udo Dietrich & Thorsten Schuetze, 2019. "Integrated BIM-Parametric Workflow-Based Analysis of Daylight Improvement for Sustainable Renovation of an Exemplary Apartment in Seoul, Korea," Sustainability, MDPI, vol. 11(9), pages 1-29, May.
    2. Nari Yoon & Mary Ann Piette & Jung Min Han & Wentao Wu & Ali Malkawi, 2020. "Optimization of Window Positions for Wind-Driven Natural Ventilation Performance," Energies, MDPI, vol. 13(10), pages 1-25, May.
    3. Dušan Katunský & Erika Dolníková & Bystrík Dolník, 2018. "Daytime Lighting Assessment in Textile Factories Using Connected Windows in Slovakia: A Case Study," Sustainability, MDPI, vol. 10(3), pages 1-20, February.
    4. Sewon Lee & Kyung Sun Lee, 2019. "A Study on the Improvement of the Evaluation Scale of Discomfort Glare in Educational Facilities," Energies, MDPI, vol. 12(17), pages 1-21, August.
    5. In-Tae Kim & Yu-Sin Kim & Meeryoung Cho & Hyeonggon Nam & Anseop Choi & Taeyon Hwang, 2019. "High-Performance Accuracy of Daylight-Responsive Dimming Systems with Illuminance by Distant Luminaires for Energy-Saving Buildings," Energies, MDPI, vol. 12(4), pages 1-21, February.
    6. Kyung Sun Lee & Ki Jun Han & Jae Wook Lee, 2017. "The Impact of Shading Type and Azimuth Orientation on the Daylighting in a Classroom–Focusing on Effectiveness of Façade Shading, Comparing the Results of DA and UDI," Energies, MDPI, vol. 10(5), pages 1-20, May.
    7. In-Tae Kim & Yu-Sin Kim & Hyeonggon Nam & Taeyon Hwang, 2018. "Advanced Dimming Control Algorithm for Sustainable Buildings by Daylight Responsive Dimming System," Sustainability, MDPI, vol. 10(11), pages 1-15, November.

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