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Impact of Building Design Parameters on Daylighting Metrics Using an Analysis, Prediction, and Optimization Approach Based on Statistical Learning Technique

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  • Jaewook Lee

    (Illinois School of Architecture, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA)

  • Mohamed Boubekri

    (Illinois School of Architecture, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA)

  • Feng Liang

    (Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA)

Abstract

Daylighting metrics are used to predict the daylight availability within a building and assess the performance of a fenestration solution. In this process, building design parameters are inseparable from these metrics; therefore, we need to know which parameters are truly important and how they impact performance. The purpose of this study is to explore the relationship between building design attributes and existing daylighting metrics based on a new methodology we are proposing. This methodology involves statistical learning. It is an emerging methodology that helps us to analyze a large quantity of output data and the impact of a large number of design variables. In particular, we can use these statistical methodologies to analyze which features are important, which ones are not, and the type of relationships they have. Using these techniques, statistical models may be created to predict daylighting metric values for different building types and design solutions. In this article we will outline how this methodology works, and analyze the building design features that have the strongest impact on daylighting performance.

Suggested Citation

  • Jaewook Lee & Mohamed Boubekri & Feng Liang, 2019. "Impact of Building Design Parameters on Daylighting Metrics Using an Analysis, Prediction, and Optimization Approach Based on Statistical Learning Technique," Sustainability, MDPI, vol. 11(5), pages 1-21, March.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:5:p:1474-:d:212598
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    References listed on IDEAS

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    1. Michael E. Tipping & Christopher M. Bishop, 1999. "Probabilistic Principal Component Analysis," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(3), pages 611-622.
    2. Vincenzo Costanzo & Gianpiero Evola & Luigi Marletta & Fabiana Pistone Nascone, 2018. "Application of Climate Based Daylight Modelling to the Refurbishment of a School Building in Sicily," Sustainability, MDPI, vol. 10(8), pages 1-19, July.
    3. Francesco Nocera & Alessandro Lo Faro & Vincenzo Costanzo & Chiara Raciti, 2018. "Daylight Performance of Classrooms in a Mediterranean School Heritage Building," Sustainability, MDPI, vol. 10(10), pages 1-15, October.
    4. 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.
    5. 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.
    6. Chen, Yibo & Tan, Hongwei, 2017. "Short-term prediction of electric demand in building sector via hybrid support vector regression," Applied Energy, Elsevier, vol. 204(C), pages 1363-1374.
    7. Francesco Leccese & Giacomo Salvadori & Matteo Casini & Marco Bertozzi, 2014. "Analysis and Measurements of Artificial Optical Radiation (AOR) Emitted by Lighting Sources Found in Offices," Sustainability, MDPI, vol. 6(9), pages 1-14, September.
    8. Lee, J.W. & Jung, H.J. & Park, J.Y. & Lee, J.B. & Yoon, Y., 2013. "Optimization of building window system in Asian regions by analyzing solar heat gain and daylighting elements," Renewable Energy, Elsevier, vol. 50(C), pages 522-531.
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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. Tony-Andreas Arntsen & Bozena Dorota Hrynyszyn, 2021. "Optimization of Window Design for Daylight and Thermal Comfort in Cold Climate Conditions," Energies, MDPI, vol. 14(23), pages 1-17, November.
    3. Ali Mohammed AL-Dossary & Daeung Danny Kim, 2020. "A Study of Design Variables in Daylight and Energy Performance in Residential Buildings under Hot Climates," Energies, MDPI, vol. 13(21), pages 1-16, November.
    4. Nataša Šprah & Mitja Košir, 2019. "Daylight Provision Requirements According to EN 17037 as a Restriction for Sustainable Urban Planning of Residential Developments," Sustainability, MDPI, vol. 12(1), pages 1-22, December.
    5. Erika Dolníková & Dušan Katunský & Zuzana Miňová & Bystrík Dolník, 2021. "Influence of the Adaptation of Balconies to Loggias on the Lighting Climate inside an Apartment Building under Cloudy Sky," Sustainability, MDPI, vol. 13(6), pages 1-24, March.

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