Journal of the Architectural Institute of Korea (대한건축학회논문집)

Architectural Institute of Korea (대한건축학회)
권호 표지
DOI QR코드

DOI QR Code

Uncertainty and Sensitivity Analyses on Solar Heat Gain Coefficient by Slat Angle of External Venetian Blinds

외부 베네시안 블라인드의 슬랫 각도에 따른 태양열 취득계수의 불확실성 및 민감도 분석

  • Lee, Jeong-Yun (Dept. of Architecture & Architectural Engineering, Seoul National University) ;
  • Kim, Young-Sub (Dept. of Architecture & Architectural Engineering, Seoul National University) ;
  • Park, Cheol-Soo (Dept. of Architecture and Architectural Engineering.Institute of Engineering Research.Institute of Construction and Environmental Engineering, Seoul National University)
  • 이정윤 (서울대 건축학과) ;
  • 김영섭 (서울대 건축학과) ;
  • 박철수 (서울대 건축학과.공학연구원.건설환경종합연구)
  • Received : 2022.10.13
  • Accepted : 2023.01.02
  • Published : 2023.02.28
⟨ Previous Next ⟩

Abstract

This study compares static vs. dynamic solar heat gain coefficient (SHGC) of a glazing system with an external blind. For many architectural design processes, static SHGC has been still widely used. The authors aim to investigate stochastic characteristics of SHGC due to slat angles and environmental conditions (direct and diffuse solar radiation, outdoor and indoor air temperature, wind velocity, etc.). For this purpose, "pyWinCalc", developed by US LBNL was employed to simulate the dynamic thermal behavior of the system. The Sobol sampling was conducted for uncertainty and sensitivity analyses of the SHGC. It was found that the variations in SHGC depending on the slat angles as well as environmental conditions are significant. The quantified stochastic characteristics of SHGC are expected to provide a rational guideline for assessing the thermal performance and optimal control of dynamic shading devices.

Keywords

Acknowledgement

본 연구는 산업통상자원부(MOTIE)와 한국에너지기술평가원(KETEP)의 지원을 받아 수행한 연구 과제입니다. (No. 20202020800360)

References

  1. ASHRAE, (2017). ASHRAE Handbook: Fundamentals 2017, ASHRAE.
  2. Augenbroe, G. (2019). The role of simulation in performance-based building. In Building performance simulation for design and operation (pp. 343-373). Routledge.
  3. Curcija C., Vidanovic S., Hart R., Jonsson J., Powles R. & Mitchell R. (2018). WINDOW Technical Documentation. Lawrence Berkeley National Laboratory. https://windows.lbl.gov/tools/window/documentation
  4. de Wilde, P. (2014). The gap between predicted and measured energy performance of buildings: A framework for investigation. Automation in construction, 41, 40-49. https://doi.org/10.1016/j.autcon.2014.02.009
  5. Hopfe, C. J., & Hensen, J. L. (2011). Uncertainty analysis in building performance simulation for design support. Energy and Buildings, 43(10), 2798-2805. https://doi.org/10.1016/j.enbuild.2011.06.034
  6. Huchuk, B., Gunay, H. B., O'Brien, W., & Cruickshank, C. A. (2016). Model-based predictive control of office window shades. Building Research & Information, 44(4), 445-455. https://doi.org/10.1080/09613218.2016.1101949
  7. ISO. (2003). Thermal performance of windows, doors and shading devices - Detailed calculations (ISO Standard No. 15099). Retrieved from https://www.iso.org/standard/26425.html
  8. Kim, D. W., & Park, C. S. (2010). Energy performance assessment of a double skin facade with different control strategies. J. Archit. Inst. Korea, 26, 389-398.
  9. Kim, D. W., & Park, C. S. (2012). Comparative control strategies of exterior and interior blind systems. Lighting Research & Technology, 44(3), 291-308.
  10. Kohler C., Mitchell R., Curcija C., Vidanovic D. & Cza rnecki S. (2019). Berkeley Lab WINDOW Calc Engine (CalcEngine) v2. https://github.com/LBNL-ETA/pyWinCalc/tree/develop
  11. KS L 9107 (2014). Testing method for the determination of solar heat gain coefficient of fenestration product using solar simulator.
  12. LBNL(Lawrence Berkeley National Laboratory). (2016). WINDOW, http://windows.lbl.gov/software/window
  13. LBNL(Lawrence Berkeley National Laboratory). (2022). IGSDB web application. Retrieved from https://igsdb.lbl.gov/
  14. Mara, T. A., & Tarantola, S. (2008). Application of global sensitivity analysis of model output to building thermal simulations. In Building Simulation (Vol. 1, No. 4, pp. 290-302). Springer Berlin Heidelberg. https://doi.org/10.1007/s12273-008-8129-5
  15. May-Ostendorp, P.T., Henze, G. P., Rajagopalan, B., & Corbin, C. D. (2012). Extraction of supervisory building control rules from model predictive control of windows in a mixed mode building. Journal of Building Performance Simulation, 6(3), 199-219.
  16. NFRC (National Fenestration Rating Council). (2020). Procedure for Determining Fenestration Product Solar Heat Gain Coefficient and Visible Transmittance at Normal Incidence (NFRC Standard No. 200). Retrieved from http://www.nfrc.org
  17. Kreider, J. F., Reddy, T. A., Curtiss, P. S., & Rabl, A. (2016). Heating and cooling of buildings: principles and practice of energy efficient design. CRC press.
  18. Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., & Tarantola, S. (2008). Global sensitivity analysis: the primer. John Wiley & Sons.
  19. Sobol, I. M. (1993). Sensitivity analysis for non-linear mathematical models. Mathematical modelling and computational experiment, 1, 407-414.