대한건축학회논문집:계획계 (Journal of the Architectural Institute of Korea Planning & Design)

  • 제32권2호
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  • Pages.213-222
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  • 2016
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  • 1226-9093(pISSN)
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  • 2384-177X(eISSN)
대한건축학회 (Architectural Institute of Korea)
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미래 기후변화를 고려한 건물 에너지 시뮬레이션의 불확실성 및 민감도 분석

Uncertainty and Sensitivity Analysis of Building Energy Simulation under Future Climate Change and Retrofit

  • 김영진 (선문대학교 건축사회환경학부) ;
  • 박철수 (성균관대학교 건축토목공학부)
  • 투고 : 2015.08.07
  • 심사 : 2016.02.05
  • 발행 : 2016.02.29
⟨ 이전 논문 다음 논문 ⟩

초록

For reliable building performance assessment, uncertainty caused by future climate change and future energy retrofit should be taken into account. This paper presents a case study of risk-based decision making driven by future uncertainty sources (climate, energy retrofit) in an existing office building. For the study, the EnergyPlus model was chosen and a Monte Carlo Sampling (MCS) technique was employed. Then, significant inputs were identified using a global sensitivity analysis. This study compares three alternatives for replacement of a chiller using deterministic and risk-based stochastic approaches. It is shown that the risk-based stochastic approach is more useful than the deterministic approach. In particular, it is highlighted that the uncertainty sources (climate, retrofit, etc.) have an important bearing on the selection of optimal alternatives.

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과제정보

연구 과제 주관 기관 : 한국연구재단

참고문헌

  1. Chan, A. L. S., & Chow, T. T. (2013). Energy and economic performance of green roof system under future climatic conditions in Hong Kong. Energy and Buildings, 64, 182-198. https://doi.org/10.1016/j.enbuild.2013.05.015
  2. de Wit, S. & Augenbroe, G. (2002). Analysis of uncertainty in building design evaluations and its implications. Energy and Buildings, 34, 951-958. https://doi.org/10.1016/S0378-7788(02)00070-1
  3. de Wilde, P. & Tian, W. (2012). Management of thermal performance risks in buildings subject to climate change. Building and Environment, 55, 167-177. https://doi.org/10.1016/j.buildenv.2012.01.018
  4. de Wilde, P. & Tian, W. (2010). Predicting the performance of an office under climate change: A study of metrics, sensitivity and zonal resolution. Energy and Buildings, 42, 1674-1684. https://doi.org/10.1016/j.enbuild.2010.04.011
  5. DOE. (2013). EnergyPlus 8.0 Input/Output Reference: The Encyclopedic Reference to EnergyPlus Input and Output, US Department Of Energy.
  6. Erbs, D. G., Klein, S. A., & Duffie, J. A. (1982). Estimation of the diffuse radiation fraction for hourly, daily, and monthly-average global radiation. Solar Energy, 28(4), 293-302. https://doi.org/10.1016/0038-092X(82)90302-4
  7. Glassman, E. J. & Reinhart, C. (2013). Facade optimization using parametric design and future climate scenarios. Proceedings of the 13th IBPSA Conference, August 25-28, Chambery, France, 1585-1592.
  8. Goldstein, M. & Rougier, J. C. (2006). Bayes linear calibrated prediction for complex systems. Journal of the American Statistical Association, 101, 1132-1143. https://doi.org/10.1198/016214506000000203
  9. Hanby, V. I. & Smith, S. T. (2012), Simulation of the future performance of low-energy evaporative cooling systems using UKCP09 climate projections. Building and Environment, 55, 110-116. https://doi.org/10.1016/j.buildenv.2011.12.018
  10. Helton, J. C. & Davis, F. J. (2003). Latin hypercube sampling and propagation of uncertainty in analyses of complex systems. Reliability Engineering and System Safety, 81, 23-69. https://doi.org/10.1016/S0951-8320(03)00058-9
  11. IBPSA. (1987-2015). Proceedings of the IBPSA (International Building Performance Simulation Association) conference ('87. '91, '93, '95, '97, '99, '01, '03, '05, '07, '09, '11, '13, '15).
  12. Iolova, K., Bernier, M., & Charneux, R. (2007). Detailed energy simulations of a net-zero energy triplex in Montreal, Proceedings of the 2nd Canadian Solar Buildings Conference, June 10-14, Calgary.
  13. IPCC. (2007). Climate change. In: Pachauri RK, Reisinger A, editors. Synthesis report. Contribution of Working Groups I, II and III to the fourth assessment report of the Intergovernmental Panel on Climate Change (IPCC). Geneva, Switzerland.
  14. Jylha, K., Jokisalo, J., Ruosteenoja, K., Pilli-Sihvola, K., Kalamees, T., Seitola, T., Mäkelä, H. M., Hyvonen, R., Laapas, M., & Drebs, A. (2015). Energy demand for the heating and cooling of residential houses in Finland in a changing climate. Energy and Buildings, 99, 104-116. https://doi.org/10.1016/j.enbuild.2015.04.001
  15. Kennedy, M. C. & O'Hagan, A. (2001). Bayesian calibration of computer models. Journal of the Royal Statistical Society, Series B, 63, Part3, 425-464. https://doi.org/10.1111/1467-9868.00294
  16. Kim, Y. J., Kim, K. C., & Park, C. S. (2014). Deterministic vs. stochastic calibration of energy simulation model for an existing building. The 2nd Asia conference of International Building Performance Simulation Association, November 28-29, Nagoya, Japan, 594-601.
  17. Kim, Y. J., Ahn, K. U., & Park, C. S. (2013). Gaussian emulator for stochastic optimal design of a double glazing system. Proceedings of the 13th IBPSA Conference, August 25-28, Chambery, France, 2217-2224.
  18. Legutke, S. & Voss, R. (1999). The Hamburg atmosphere-ocean coupled model ECHO-G. Technical Report 18, German Climate Computer Center (DKRZ).
  19. Lomas, K. L. & Eppel, H. (1992). Sensitivity analysis techniques for building thermal simulation programs. Energy Buildings, 19, 21-44. https://doi.org/10.1016/0378-7788(92)90033-D
  20. 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, 992-999.
  21. Min, S. K., Legutke, S., Hense, A., & Kwon, W. T. (2005). Internal variability in a 1000-yr control simulation with the coupled climate model ECHO-G-I. Near-surface temperature, precipitation and mean sea level pressure. Tellus, 57A, 605-621.
  22. Neal, R. M. (1997). Monte carlo implementation of gaussian process models for Bayesian regression and classification, Technical Report 9702, Dept. of statistics and Dept. of Computer Science, University of Toronto.
  23. Oakley, J. & O'Hagan, A. (2004). Probabilistic sensitivity analysis of complex models: a bayesian approach. Journal of the Royal Statistical Society, Series B 66, 751-769. https://doi.org/10.1111/j.1467-9868.2004.05304.x
  24. Park, E. H. & Kwon, W. T. (2004). Future projections of east asian climate change from multi-AOGCM ensembles of IPCCSRES scenario simulations. Journal of the Meteorological Society of Japan, 82(4), 1187-1211. https://doi.org/10.2151/jmsj.2004.1187
  25. Park, S. Y., Park, H. S., Lee, S. H., Kim, J. Y., & Hong, S. H. (2010). A study on the optimization of heating and cooling system university campus, Korea Institute of Ecological Architecture and Environment, 10(6), 139-144.
  26. Perry, M. & Hollis, D. (2005). The generation of monthly gridded datasets for a range of climatic variables over the UK. International Journal of Climatology, 25, 1041-1054. https://doi.org/10.1002/joc.1161
  27. Petersen, S. & Bundgaard, K. W. (2014). The effect of weather forecast uncertainty on a predictive control concept for building systems operation. Applied Energy, 116, 311-321. https://doi.org/10.1016/j.apenergy.2013.11.060
  28. Robert, A. & Kummert, M. (2012). Designing net-zero energy buildings for the future climate, not for the past. Building and Evironmnet, 55, 150-158.
  29. Roeckner, E., Arpe, K., Bengtsson, L., Christoph, M., Claussen, M., Dumenil, L., Esch, M., Giorgetta, M., Schlese, U., & Schulzweida, U. (1996). The atmospheric general circulation model ECHAM-4: model description and simulation of present-day climate Max-Planck Institute for Meteorology. Report No.218, Hamburg, Germany.
  30. Saltelli, A., Tarantola, S., Campolongo, F., & Ratto, M. (2004). Sensitivity Analysis in Practice, Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England
  31. Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global sensitivity analysis, John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England.
  32. Wolff, J., Maier-Reimer, E., & Legutke, S. (1997). The Hamburg Primitive Equation Model HOPE. Technical Report 18, German Climate Computer Center (DKRZ).
  33. Yu, F. W., Chan, K. T., & Sit, R. K. Y. (2012). Climate influence on the design and operation of chiller systems serving office buildings in a subtropical climate. Energy and Buildings, 55, 500-507. https://doi.org/10.1016/j.enbuild.2012.08.022