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Genetic algorithms for ceiling form optimization in response to daylight levels

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  • Rakha, Tarek
  • Nassar, Khaled

Abstract

Utilization of daylight in indoor spaces creates opportunities for energy savings and is linked to increasing productivity in the workplace. This sensitive aspect in environmentally aware architectural design depends on many interfacing factors. In this research, ceiling geometry is investigated as an element that can provide control to natural light, achieved through reflection and diffusion of the external and internal reflected components of daylight. This paper presents a generic optimization procedure for architects that aids in generation and finding of curvilinear and mesh ceiling forms. The objective was to maximize daylight uniformity ratios. A genetic algorithm was developed and coded in LUA, a versatile scripting language. Radiance simulation software was employed as the backend daylighting performance calculation engine, and Ecotect as the front end form input and visualization tool. Conclusions about the optimum ceiling geometry and form for a designed example case were drawn. The presented method provided architects with a variety of choices for designs which are weighed through daylighting performance. Results showed that this approach offers a robust and yet precise form finding method.

Suggested Citation

  • Rakha, Tarek & Nassar, Khaled, 2011. "Genetic algorithms for ceiling form optimization in response to daylight levels," Renewable Energy, Elsevier, vol. 36(9), pages 2348-2356.
    • Handle: RePEc:eee:renene:v:36:y:2011:i:9:p:2348-2356
    DOI: 10.1016/j.renene.2011.02.006
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    References listed on IDEAS

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    1. Freewan, Ahmed A. & Shao, Li & Riffat, Saffa, 2009. "Interactions between louvers and ceiling geometry for maximum daylighting performance," Renewable Energy, Elsevier, vol. 34(1), pages 223-232.
    2. Tsangrassoulis, A. & Bourdakis, V. & Geros, V. & Santamouris, M., 2006. "A genetic algorithm solution to the design of slat-type shading system," Renewable Energy, Elsevier, vol. 31(14), pages 2321-2328.
    3. Wong, S.L. & Wan, Kevin K.W. & Lam, Tony N.T., 2010. "Artificial neural networks for energy analysis of office buildings with daylighting," Applied Energy, Elsevier, vol. 87(2), pages 551-557, February.
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    Cited by:

    1. Yibing Xue & Wenhan Liu, 2022. "A Study on Parametric Design Method for Optimization of Daylight in Commercial Building’s Atrium in Cold Regions," Sustainability, MDPI, vol. 14(13), pages 1-22, June.
    2. Mangkuto, Rizki A. & Rohmah, Mardliyahtur & Asri, Anindya Dian, 2016. "Design optimisation for window size, orientation, and wall reflectance with regard to various daylight metrics and lighting energy demand: A case study of buildings in the tropics," Applied Energy, Elsevier, vol. 164(C), pages 211-219.
    3. Machairas, Vasileios & Tsangrassoulis, Aris & Axarli, Kleo, 2014. "Algorithms for optimization of building design: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 101-112.

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