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Development of Sky Luminance and Daylight Illuminance Prediction Methods for Lighting Energy Saving in Office Buildings

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  • Chul-Ho Kim

    (Department of Architecture, College of Engineering, Korea University, 145 Anam–Ro, Seongbuk-Gu, Seoul 02841, Korea)

  • Kang-Soo Kim

    (Department of Architecture, College of Engineering, Korea University, 145 Anam–Ro, Seongbuk-Gu, Seoul 02841, Korea)

Abstract

Accurately predicting indoor illuminance from daylight during the early stages of building design is an important factor in saving energy and the costs associated with lighting. The objective of this study was to predict sky luminance distribution using the Commission Internationale de l’éclairage (CIE) standard sky model, and propose a method that can be used to predict indoor illuminance. Results obtained from the proposed prediction method were compared and verified with simulation values obtained by Desktop Radiance. From the CIE overcast sky, the zenith/horizon ratio was 3:1. From the CIE clear sky, the luminance value was highest around the sun. In contrast, the luminance value was lowest in the opposite direction of the sun when the angle between the sun and sky elements was 90°. In addition, this study suggested an indoor illuminance prediction method by applying the effects of sky luminance, direct sunlight, and wall reflection elements. When the proposed equation’s calculation results were compared with Desktop Radiance simulation’s value in overcast and clear sky, all statistically analysis (R 2 , MBE, Cv(RMSE), t -value, p -value) satisfied each standard and showed high correlations. Consequently, it was established that the predicted indoor illuminance obtained from the proposed prediction method was accurate and can be used to predict the level of indoor illuminance. The results further revealed that it is possible to calculate indoor illuminance when installing blinds, by substituting variable values of visible light transmittance (VLT).

Suggested Citation

  • Chul-Ho Kim & Kang-Soo Kim, 2019. "Development of Sky Luminance and Daylight Illuminance Prediction Methods for Lighting Energy Saving in Office Buildings," Energies, MDPI, vol. 12(4), pages 1-37, February.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:4:p:592-:d:205482
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    References listed on IDEAS

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    Cited by:

    1. Lou, Siwei & Huang, Yu & Li, Danny H.W. & Xia, Dawei & Zhou, Xiaoqing & Zhao, Yang, 2020. "A novel method for fast sky conditions identification from global solar radiation measurements," Renewable Energy, Elsevier, vol. 161(C), pages 77-90.
    2. Panagiotis Korkidis & Anastasios Dounis & Panagiotis Kofinas, 2021. "Computational Intelligence Technologies for Occupancy Estimation and Comfort Control in Buildings," Energies, MDPI, vol. 14(16), pages 1-33, August.
    3. Chul-Ho Kim & Kwang-Ho Lee & Kang-Soo Kim, 2020. "Evaluation of Illuminance Measurement Data through Integrated Automated Blinds and LED Dimming Controls in a Full-Scale Mock-Up," Energies, MDPI, vol. 13(12), pages 1-34, June.
    4. Jiraphorn Mahawan & Atthakorn Thongtha, 2021. "Experimental Investigation of Illumination Performance of Hollow Light Pipe for Energy Consumption Reduction in Buildings," Energies, MDPI, vol. 14(2), pages 1-17, January.
    5. Lou, Siwei & Li, Danny H.W. & Alshaibani, Khalid A. & Xing, Haowei & Li, Zhengrong & Huang, Yu & Xia, Dawei, 2022. "An all-sky luminance and radiance distribution model for built environment studies," Renewable Energy, Elsevier, vol. 190(C), pages 822-835.
    6. Atthakorn Thongtha & Piromporn Boontham, 2020. "Experimental Investigation of Natural Lighting Systems Using Cylindrical Glass for Energy Saving in Buildings," Energies, MDPI, vol. 13(10), pages 1-12, May.

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