DOI QR코드

DOI QR Code

Bayesian Calibration of Energy Model for Existing Buildings

기축 건물의 에너지 모델에 대한 베이지안 보정

  • 윤성환 (성균관대 건설환경시스템공학과) ;
  • 김영진 (선문대 건축사회환경학부) ;
  • 박철수 (성균관대 건축토목공학부)
  • Received : 2014.07.21
  • Accepted : 2014.09.05
  • Published : 2014.10.30

Abstract

Energy consumption by existing buildings accounts for a significant portion of domestic energy use. To improve energy performance of existing buildings, it is necessary to have a first principle based energy model for building energy performance assessment as well as rational decision making for energy retrofit. Over the past several decades, building energy calculation theories and applications have been significantly developed. However, such high-end building energy simulation tools require demanding time, cost and modeling efforts. In addition, as simulation tools become sophisticated and complicated, uncertainty caused by subjective judgment, modeling assumptions and stochastic building behavior is also not negligible, followed by a so-called 'performance gap' between prediction and a reality. To solve the aforementioned issues, the authors present application of Bayesian calibration, a stochastic parameter estimation technique to ISO 13790 model for an existing office buildings. In the paper, it is addressed that such simple energy model (ISO 13790) can produce accurate prediction when enhanced with Bayesian calibration and the calibrated model can be beneficially used for energy retrofit decision making.

Keywords

Acknowledgement

Supported by : 국토교통부

References

  1. 대한설비공학회 (2004), 설비공학편람
  2. 박철수 (2006), 규범적 건물성능평가방법, 대한건축학회논문집 계획계 제22권 11호, p.p.337-344
  3. 성백진(역), 공조설비 실무 퍼펙트 매뉴얼, 에코북, 2013
  4. 에너지관리공단 (2012), 진단기관 건축물에너지 효율평가프로그램 보수교육 자료
  5. 윤성환, 박철수 (2014), 기존 건축물을 위한 x-Ray 개념의 에너지 모델 작성과 평가, 대한건축학회논문집 계획계 제30권 1호, p.p.235-244 https://doi.org/10.5659/JAIK_PD.2014.30.1.235
  6. 이동현, 김영진, 박철수, 김인한 (2013), 베이지언 확률적 보정을 이용한 에너지 시뮬레이션, 대한건축학회 논문집 계획계 제29권 2호, p.p.243-250
  7. ASHRAE (2002), ASHRAE Guildeline 14: Measurement of energy and demand savings, Atlanta
  8. ASHRAE (2009), ASHRAE Handbook Fundamentals, American Society of Heating, Refrigerating and Air-Conditioning Engineers Inc., Atlanta, GA
  9. Bates, B. C. and Campbell, E. P. (2001), A Markov chain Monte Carlo scheme for parameter estimation and inference in conceptual rainfall-runoff modelling, Water Resources Research, Vol.37, No.4, p.p.937-947 https://doi.org/10.1029/2000WR900363
  10. Bayes. T. (1763), An essay towards solving a problem in the doctrine of chances, Philos Trans R Soc Lond, Vol.53, p.p.370-418 https://doi.org/10.1098/rstl.1763.0053
  11. Bishop, C. M. (2006), Pattern Recognition and Machine Learning, New York: Springer
  12. Booth, A. T., Choudhary, R. and Spiegelhalter, D. J. (2012), Handling uncertainty in housing stock models, Building and Environment, Vol.48, p.p.35-47 https://doi.org/10.1016/j.buildenv.2011.08.016
  13. Coakley, D., Raftery, P., Molloy, P. and White, G. (2011), Calibration of a detailed BES model to measured data using an evidence-based analytical optimisation approach, Proceedings of the 12th IBPSA Conference (International Building Performance Simulation Association), November 14-16, Sydney, Australia, p.p.374-381
  14. DIN V 18599-2 (2007), Energy efficiency of buildings-Calculation of the energy needs, delivered energy and primary energy for heating, cooling, ventilation, domestic hot water and lighting-Part 2: Energy needs for heating and cooling of building zones
  15. Dowd, M. and Meyer, R. (2003), A Bayesian approach to the ecosystem inverse problem, Ecological Modelling, Vol.168, p.p.39-55 https://doi.org/10.1016/S0304-3800(03)00186-8
  16. EN ISO 13790 (2008), Energy performance of buildings - Calculation of energy use for space heating and cooling
  17. EN ISO 6946 (2008), Building components and building elements-Thermal resistance and thermal transmittance-Calculation method
  18. EN 15193 (2008), Energy performance of buildings-Energy requirements for lighting
  19. EN 15243 (2007), Ventilation for buildings-Calculation of room temperatures and of load and energy for buildings with room conditioning systems
  20. EN 15316-2-3 (2007), Heating systems in buildings-Method for calculation of system energy requirements and system efficiencies-Part 2-3: Space heating distribution systems
  21. EN 15316-3-1 (2008), Heating systems in buildings-Method for calculation of system energy requirements and system efficiencies-Part 3-1: Domestic hot water systems, characterisation of needs
  22. EN 15316-3-2 (2008), Heating systems in buildings-Method for calculation of system energy requirements and system efficiencies-Part 3-2: Domestic hot water systems, distribution
  23. Hensen, J. L. M. and Lamberts, R. (2011), Building Performance Simulation for Design and Operation, Taylor and Francis
  24. Heo, Y. S. (2011), Bayesian calibration of building energy models for energy retrofit decision-making under uncertainty, Ph.D. thesis, Georgia Institute of Technology, Atlanta, GA, USA
  25. Higdon, D., Nakhleh, C., Gattiker, T. and Williams, B. (2008), A Bayesian calibration approach tp approach to the thermal problem, Comput. Methods in Appl. Mech. Engrg. Vol.197, p.p.2431-2441 https://doi.org/10.1016/j.cma.2007.05.031
  26. IBPSA, Proceedings of the International Building Performance Simulation Association Conferences, (87, 91, 93, 95, 97, 99, 2001, 03, 05, 07, 09, 11, 13), 1987-2013
  27. Jiang, X. and Mahadevan, S. (2007), Bayesian risk-based decision method for model validation under uncertainty, Reliability Engineering and System Safety, Vol.92, p.p.707-718 https://doi.org/10.1016/j.ress.2006.03.006
  28. Macdonald, I. A. (2002), Quantifying the effects of uncertainty in building simulation, Ph.D. thesis, University of Strathclyde, Scotland
  29. Maile, T. (2010), Comparing measured and simulated building energy performance data, Ph.D. thesis, Standard University
  30. Morris, M. D. (1991), Factorial sampling plans for preliminary computational experiments, Technometrics, Vol.33, p.p.161-174 https://doi.org/10.1080/00401706.1991.10484804
  31. NEN 2916 (1998), Energy performance of non-residential buildings, determination method
  32. Park, C. S., Augenbroe, G., Messadi, T., Thitisawat, M. and Sadegh, N. (2004), In-situ Calibration of a Lumped Simulation Model of Smart Double-Skin Facade Systems, Energy and Buildings, Vol.36, No.11, p.p.1117-1130 https://doi.org/10.1016/j.enbuild.2004.04.003
  33. Reddy, T. A. and Maor, I., Jian, S. and Panjapornporn, C. (2006), Procedures for Reconciling Computer-Calculated Results With Measured Energy Data, ASHRAE Research Project 1051-RP
  34. Swiler, L. P. (2006), Bayesian Methods in Engineering Design Problems, Sandia National Laboratories report, SAND: 2005-3294
  35. Wang, Q. (2001), A Bayesian joint probability approach for flood record augmentation, Water Resources Research, Vol.37, No.6, p.p.1707-1712 https://doi.org/10.1029/2000WR900401
  36. Yoon, S. H., Park, C. S and Augenbroe, G. (2011), On-line parameter estimation and optimal control strategy of a double-skin system, Building and Environment, Vol.46, p.p.1141-1150 https://doi.org/10.1016/j.buildenv.2010.12.001
  37. Zellner, A. (1971), An Introduction to Bayesian inference in econometrics, New York, Wiley