DOI QR코드

DOI QR Code

Comparison of Deterministic and Stochastic Approaches in Building Energy Demands - Focused on Usage Profiles -

건물 에너지요구량의 결정적 접근과 확률적 접근의 비교 - 용도프로필을 중심으로 -

  • Lee, Seung-Ju (Dept. of Architecture & Architectural Engineering, Seoul National University) ;
  • Yoo, Young-Seo (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)
  • 이승주 (서울대 건축학과) ;
  • 유영서 (서울대 건축학과) ;
  • 박철수 (서울대 건축학과.공학연구원.건설환경종합연구소)
  • Received : 2022.05.11
  • Accepted : 2022.07.12
  • Published : 2022.08.30

Abstract

ECO2 is the de facto building energy calculation tool in South Korea; it prescribes deterministic usage profiles such as people, lighting, equipment and ventilation. However, it has been widely acknowledged that such deterministic approaches cannot take into account stochastic thermal behavior in buildings. With this in mind, the authors investigated the difference in building energy prediction between deterministic and stochastic approaches. For the stochastic approach, EnegyPlus, one of the most advanced dynamic building simulation tools, was selected. Latin Hypercube Sampling was employed to generate 1,000 simulation cases of the DOE medium-office reference building. It is found that there is a significant difference between deterministic and stochastic approaches. It is suggested that the stochastic approach must be considered for rational decision making of building energy certification rating.

Keywords

Acknowledgement

해당 과제는 한국에너지공단의 연구비를 지원받아 수행되었습니다.

References

  1. Ahn, K. U., Kim, Y. J., & Park, C. S. (2012). Issues on dynamic building energy performance assessment in design process. Journal of the Architectural Institute of Korea Planning & Design, 28(12), 361-369.
  2. ASHRAE, (2013). ASHRAE Handbook: Fundamentals 2013, ASHRAE, 461.
  3. CIBSE. (2006). Environmental design, The Chartered Institution of Building Services Engineers, London.
  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. (2009). Uncertainty and sensitivity analysis in building performance simulation for decision support and design optimization. PhD diss., Eindhoven University.
  6. Hwang, J. S., & Kim, K. S. (2009). Energy performance evaluation of a office building using DOE-2 simulation program. Proceedings of KIAEBS, 238-243.
  7. Im, M., Kwon, W., & Lee, J. (1995). Two-Stage Latin Hypercube Sampling and its application, The Korean Journal of Applied Statistics, 8(2), 99-108.
  8. KEA. (n.d). Building energy efficiency certification, Korea Energy Agency, Retrieved June 1, 2022 from http://www.kemco.or.kr/web/kem_home_new/ener_efficiency/building_02.asp
  9. Kim, Y. J., & Park, C. S. (2008). Uncertainty analysis of ventilation strategies in residential apartment buildings. Journal of the Architectural Institute of Korea, Planning and Design section, 24(8), 311-320.
  10. Korea Institute of Construction Technology. (2014). Con struction report/Published data. Codil. Retrieved June 1, 2022 from https://www.codil.or.kr/viewDtlConRpt.do?gubun=rpt&pMetaCode=OTKCRK180189
  11. Mckay, M. D., Beckman, R. J., & Conover, W. J. (1979). A comparison of three methods for selecting values of input variables in the analysis of ouput from a computer code, Technometrics, Vol. 21, No. 2, 239-245.
  12. Pan, Y., Huang, Z., & Wu, G. (2007). Calibrated building energy simulation and its application in a high-rise commercial building in Shanghai. Energy and Buildings, 39(6), 651-657. https://doi.org/10.1016/j.enbuild.2006.09.013
  13. Park, C. S. (2006). Normative assessment of technical building performance. Journal of Architecture Institute (Planning), 22(11), 337-344.
  14. Shi, X., Si, B., Zhao, J., Tian, Z., Wang, C., Jin, X., & Zhou, X. (2019). Magnitude, causes, and solutions of the performance gap of buildings: A review. Sustainability, 11(3), 937. https://doi.org/10.3390/su11030937
  15. Yoo, Y. S., Yi, D. H., Kim, S. S., & Park, C. S. (2020). Rational building energy assessment using global sensitivity analysis. Journal of the Architectural Institute of Korea Structure & Construction, 36(5), 177-185.
  16. Yoo, Y. S., Yi, D. H., & Park, C. S. (2021). Uncertainty in sensitivity analysis of architectural design variables for heating and cooling loads depending on usage scenarios. Journal of the Architectural Institute of Korea, 37(11), 247-253. https://doi.org/10.5659/JAIK.2021.37.11.247
  17. Yoon, S. D., Park, S. H., & Sohn, J. Y. (2008). Case study of energy performance evaluation in office building. Journal of The Society of Living Environment System, 15(4), 447-453.
  18. Yun, J. (2021, September, 27). Play separately from the best grade of green building and actual energy requirements, Electimes. https://www.electimes.com/news/articleView.html?idxno=223051