DOI QR코드

DOI QR Code

전열교환 환기시스템의 설계 지원을 위한 확률적 다기준 의사 결정 접근의 적용

Application of Stochastic Multi-Criteria Decision Making Approach for Design Support of Energy Recovery Ventilator

  • Kim, Young-Jin (Division of Architecture, Architectural Engineering and Civil Engineering, Sunmoon University)
  • 투고 : 2017.01.18
  • 심사 : 2017.03.24
  • 발행 : 2017.04.30

초록

Recently, a Energy Recovery Ventilator(ERV) in a residential building has been highlighted as an attractive ventilation option in terms of energy saving and Indoor Air Quality(IAQ). For identifying a feasible set among many ventilation strategies in this situation, various decision making approaches(deterministic or stochastic) using building simulation tools have been suggested. In the simulation based decision making approaches, this paper addresses a Stochastic Multi-Criteria Decision Making(SMCDM) method based on Cumulative Prospect Theory(CPT) for finding a preferred ventilation strategy under model uncertainties. For this study, two ventilation strategies considering air inlet positions and $CO_2$ sensor positions were chosen and modelled using two simulation tools(CONTAMW 3.1 for an air-flow model and EnergyPlus for a thermal model). And Latin Hypercube Sampling(LHS) was used to reflect model uncertainties. In this study, it is shown that CPT can lead to better a realistic and trustworthy framework, rather than Bayesian decision theory mentioned in a building simulation domain.

키워드

과제정보

연구 과제 주관 기관 : 한국연구재단

참고문헌

  1. ASHRAE. (2013). ASHRAE Handbook Fundamentals. Atlanta: American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc.
  2. de Wit, S. (2001). Uncertainty in prediction of thermal comfort in Buildings, Ph.D. Dissertation, Tu Delft Netherlands.
  3. DOE (2013a). EnergyPlus 8.0 Input/Output Reference: The Encyclopedic Reference to EnergyPlus Input and Output, US Department Of Energy.
  4. DOE (2013b). EnergyPlus 8.0 Engineering Reference: The Encyclopedic Reference to EnergyPlus Calculations, U.S. Department Of Energy.
  5. Gang, W., Wang, S., Shan, K., & Gao, D. (2015). Impacts of cooling load calculation uncertainties on the design optimization of building cooling systems, Energy and Buildings, 94, 1-9.
  6. Heo, Y.S. (2011). Bayesian calibration of building energy models for energy retrofit decision-making under uncertainty, Ph.D. Dissertation, Georgia Institute of Technology, Atlanta, GA. USA.
  7. Hopfe, C.J. (2009). Uncertainty and sensitivity analysis in building performance simulation for decision support and design optimization, Ph.D. Dissertation, Technische Universiteit Eindhoven.
  8. Hu, J., & Yang, L. (2011). Dynamic stochastic multi-criteria decision making method based on cumulative prospect theory and set pair analysis, System Engineering Procedia, 1, 432-439. https://doi.org/10.1016/j.sepro.2011.08.064
  9. Hu, J., Chen, P., & Yang, L. (2014). Dynamic Stochastic Multi-Criteria Decision Making Method Based on Prospect Theory and Conjoint Analysis, Management Science and Engineering, 8(3), 65-71.
  10. Hyun, S.H., & Park, C.S. (2006). Uncertainty analysis of natural ventilation phenomena(1st year report submitted to the Korea Institute of Construction & Transportation Technology Evaluation and Planning(KICTEP)).
  11. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk, Econometrica: Journal of the Econometric Society, 47(2), 263-291. https://doi.org/10.2307/1914185
  12. 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.
  13. Kim, Y.J., Park, C.S., & Kim, I.H. (2012). Sampling Methods and Stochastic Inference in Monte Carlo Building Simulation, Journal of the Architectural Institute of Korea, Planning and Design section, 28(6), 227-236.
  14. Kim, Y.J., Ahn, K.U., & Park, C.S. (2014). Decision Making of HVAC System using Bayesian Markov Chain Monte Carlo method, Energy and Buildings, 72, 112-121. https://doi.org/10.1016/j.enbuild.2013.12.039
  15. Krohling, R.A., & de Souza, T.T.M. (2012). Combining prospect theory and fuzzy numbers to multi-criteria decision making, Expert Systems with Applications, 39, 11487-11493. https://doi.org/10.1016/j.eswa.2012.04.006
  16. KS. (2003). KS F 2292-88: The testing method of airtightness for windows and doors.
  17. Lahdelma, R., & Salminen, P. (2009). Prospect theory and stochastic multicriteria analysis (SMAA), Omega, 37, 961-971. https://doi.org/10.1016/j.omega.2008.09.001
  18. Laverge, J., & Janssens, A. (2013). Optimization of design flow rates and component sizing for residential ventilation, Building and Environment, 65, 81-89. https://doi.org/10.1016/j.buildenv.2013.03.019
  19. Lee, D.H. (2014). Prospect Theory based NPC Decision Making Model on Dynamic Terrain Analysis, Journal of Korea Game Society, 14(4), 37-44. https://doi.org/10.7583/JKGS.2014.14.4.37
  20. Lee, Y.G. (1997). A study on the prediction model of ventilation performance for multi-family housing projects using airflow analysis, Ph.D. Dissertation, Yonsei University.
  21. MacDonald, I.A. (2002). Quantifying the effects of uncertainty in building simulation, Ph.D. thesis, University of Strathclyde, Scotland.
  22. Macdonald, I.A. (2009). Comparison of sampling techniques on the performance of monte-carlo based sensitivity analysis, Proceedings of the 11th IBPSA Conference , July 27-30, Glasgow, Scotland, pp.992-999.
  23. Prelec, B.D. (1998). The probability weighting function, Econometrica, 66(3), 497-527. https://doi.org/10.2307/2998573
  24. Sun, Y., Gu, L., Wu, C.F.J., & Augenbroe, G. (2014). Exploring HVAC system sizing under uncertainty, Energy and Buildings, .81, 243-252.
  25. Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty, Journal of Risk and Uncertainty, 5(4), 297-323. https://doi.org/10.1007/BF00122574
  26. Von Neumann, J., & Morgenstern, O. (1947). Theory of Games and Economic Behavior, Princeton University Press, Princeton, New Jersey.
  27. Walton, G.N., & Dols, W.S. (2005). CONTAMW 2.4 User Guide and Program Documentation, NISTIR 7251, Gaithersburg, MD, National Institute of Standards and Technology.
  28. Wakker, P.P. (2010). Prospect Theory: for risk and ambiguity, Cambridge University Press, UK.
  29. Zeng, J.M. (2007). An experimental test on cumulative prospect theory, Journal of Jinan University (Nature Science Edition), 28(1), 44-47.