Journal of the Architectural Institute of Korea (대한건축학회논문집)

Architectural Institute of Korea (대한건축학회)
권호 표지
DOI QR코드

DOI QR Code

Integrated Design for Rooftop Daylighting of Sports Hall by Multi-purpose Optimization Process with daylighting and energy performance

자연채광 및 에너지 복합성능 최적화 프로세스를 통한 체육관 천창 통합설계

  • Received : 2022.08.07
  • Accepted : 2022.09.21
  • Published : 2022.10.30
⟨ Previous Next ⟩

Abstract

This study aimed to analyze multi-purpose optimized design alternatives for sports halls to minimize total energy consumption, maximize daylight quantity, minimize glare probability and develop meta-models to predict energy and daylight performance during the early design stage. The automatic optimization tool of Modefrontier integrated with the rhino-grasshopper model was developed and simulated with the energy plus and radiance engine. Three optimization phases were conducted, and the variable ranges and optimization algorithms were selected for each phase's aim. In the first and second phases, the optimized cases were selected in the Pareto surfaces and compared to analyze the influence of glare prevention on the best-performing cases. Lastly, the meta-model was developed and presented to predict energy and daylight performance with a variation of the three most sensitive variables to predict the performance without energy simulation by architects and all participants. The rooftop daylighting model with cone-type lightwell was selected for the analysis with four geometric variables and two material variables for parametric design development. The results revealed that the window-floor-ratio was a dominant variable for all energy, useful daylight index, and daylight glare probability followed by tilting height and lightwell height. The window-floor ratio in the Pareto-optimized cases ranged between 11 and 21 percent in the first optimization without the glare-free objective; the range was reduced to between 8.5 and 14 percent. The range of lightwell height shrunk between 360 and 480 mm to between 240 and 360 in relieving glare. The developed response surface model with restricted window-floor ratio of 9.5-13 percent is expected to provide relevant information for future decision-making purposes.

Keywords

References

  1. Alhagla, K., Mansour, A. & Elbassuoni, R. (2019). Optimizing windows for enhancing daylighting performance and energy saving, Alexandria Engineering Journal, 58(1), 283-290. https://doi.org/10.1016/j.aej.2019.01.004
  2. Aydin, E., Dursun, O., Chatzikonstantinou, I., & Ekici B. (2015). Optimisation of energy consumption and daylighting using building performance surrogate model, 49th International Conference of the Architectural Science Association, 536-546.
  3. Chen, X & Yang H. (2017). A multi-stage optimization of passively designed high-rise residential buildings in multiple building operation scenarios, Applied Energy, 206, 541-557. https://doi.org/10.1016/j.apenergy.2017.08.204
  4. CIBSE:SLL. (2011). Lighting Guide 5-Lighting for Education.
  5. Climate Onebuilidng Organization.(n.d). https://climate.onebuilding.org, (acessed March 15, 2022).
  6. CSN EN 12193: Light and lighting - Sports lighting, 2018.
  7. Dharwal J., & Banerjee R. (2017). An approach for building design optimization using design of experiments, Building Simulation, 10, 323-336. https://doi.org/10.1007/s12273-016-0334-z
  8. Education funding agency (2014). EFA daylight design guide.
  9. European committee for standardization, European daylight standard (EN 17037), 2018.
  10. Ghobad, L., Place W., & Cho S.(2013). Design optimization of daylight roofing systems: roof monitors with glazing facing in two opposite directions, 13th Conference of International Building Performance Simulation Association, 1600-1607.
  11. Groen T., L'ambert G., Bellini R., Chaskopoulou A., Petric D., Zgomba M., Marrama L., & Bicout D.(2017). Ecology of West Nile virus across four European countries: empirical modelling of the Culex pipiens abundance dynamics as a function of weather, Biomed Central, 1-11.
  12. Hiyama K. & Wen L. (2015). Rapid response surface creation method to optimize window geometry using dynamic daylighting simulation and energy simulation, Energy and Buildings, 107, 417-423. https://doi.org/10.1016/j.enbuild.2015.08.035
  13. Korean Agency for Technology and Standards. (2018). Recommended levels of illuminace.
  14. Mardaljevic, J. (2000). The Simulation of Annual Daylighting Profiles for Internal Illuminance, Lighting Research and Technology, 32(3), 111-118. https://doi.org/10.1177/096032710003200302
  15. Ministry of Land, Infrastructure, and Transport, (2021). Green Building Certification Criteria.
  16. Shahbazi Y., Heydari, M., & Haghparast, F. (2019). An early-stage design optimization for office buildings' facade providing high-energy performance and daylight, Indoor and Built Environment, 28(10), 1350-1367. https://doi.org/10.1177/1420326X19840761
  17. Sun, C., Liu, Q., & Han., Y. (2020). Many-objective optimization design of a public building for energy, daylighting and cost performance improvement, Applied Science, 2020(10), 2435-2458.
  18. Reinhart, C. F., & Herkel, S. (2000). The simulation of annual daylight illuminance distributions - A state-of-the-art comparison of six RADIANCE-based methods, Energy and Buildings, 32(2), 167-187. https://doi.org/10.1016/S0378-7788(00)00042-6
  19. UK National Calculation Methodology (2022). NCM Databases, https://www.uk-ncm.org.uk/(acessed May 15 2022).
  20. USGBC. (n.d). LEED Credit Library, https://www.usgbc.org/credits (acessed May 15 2022).
  21. Wu Y., Zhan Q., & Quan S. (2021). A robust metamodel-based optimization design method for improving pedestrian wind comfort in an infill development project, Sustainable Cities and Society, 72, 103018. https://doi.org/10.1016/j.scs.2021.103018
  22. Zhao, Y. , Li, Y., Chen, X., Xia D., Huang, Y., & Lou, S. (2022). Parametric optimization procedure for efficient window design of educational buildings in the Pearl River Delta of China, International Journal of Low-Carbon Technologies, 2022(17), 394-410.