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Feasibility of Building Energy Simulation to Architectural and Engineering Design

건물 에너지 시뮬레이션의 유용성

  • Received : 2016.09.23
  • Accepted : 2017.04.07
  • Published : 2017.05.30

Abstract

Building energy performance simulation (BEPS) has been developed over the last decades. Substantial attempts have been made to enhance its capability and to transfer its technology into design practice. Despite these efforts, the BEPS tools are not pervasively used on daily basis during the design process for better decision making. Moreover, some skeptical researchers raise a question whether the BEPS tools are truly useful or not with regard to designers' modus operandi. Such situation has raised doubts on the feasibility of the BEPS tools. The reasons for this situation include the following: inappropriate understanding with regards to the limitations of the simulation model, simulation tools, uncertainty of the reality, model complexity, user interface, etc. In this paper, the aforementioned causes are explored from the perspective of architectural and engineering designers. A concept of a net model capability is proposed, and it is explained when and how the simulation work can be relevant to design activities.

Keywords

Acknowledgement

Supported by : 국토교통부

References

  1. Attia, S., Hensen, J.L.M., Beltranc, L., & Herde, A.D. (2012). Selection criteria for building performance simulation tools: contrasting architects' and engineers' needs. Journal of building performance simulation, 5(3), 155-169 https://doi.org/10.1080/19401493.2010.549573
  2. Augenbroe, Brown, J., Heo1, Y.S., Kim, S.H., Li, Z.,McManus, S., & Zhao, F. (2008). Lessons from an Advanced Building Simulation Course. Proceedings of the 3rd National Conference on Building Simulation, Berkeley, California, July 30-August 1, 2008
  3. Augenbroe (2015). Post BS2015 reflections, discussions of recent work. Seminar at Sungkyunkwan University, South Korea, Dec. 2015
  4. Bellinger, G. (2004). Systems Thinking "A journey in the realm of systems". Retrieved Mar. 2016 from http://www.systems-thinking.org
  5. Charles, P.P., & Thomas C.R. (2009). Four approaches to teaching with building performance simulation tools in undergraduate architecture and engineering education. Journal of Building Performance Simulation, 2(2), 95-114 https://doi.org/10.1080/19401490802592798
  6. Clarke, J. (2001). Energy Simulation in Building Design, 1st ed., Taylor & Francis,
  7. Clarke, J., & Hensen. J.L.M. (2015). Integrated Building Performance Simulation: Progress, Prospects and Requirements. Building and Environment, 91, 294-306 https://doi.org/10.1016/j.buildenv.2015.04.002
  8. Cooper, A. Reimann R, Cronin, D. (2007). About Face3: The Essentials of Interaction Design. 3rd ed., Wiley Publishing Inc.
  9. Crawley D.B., Hand J.W., Kummert M., & Griffith B.T. (2005). Contrasting the capabilities of building energy performance simulation programs. Proceedings of the 9th International Conference on Building Simulation, Montreal, Canada, August 15-18
  10. Donn, M. (2001). Tools for quality control in simulation. Building and Environment, 36(6), 673-680 https://doi.org/10.1016/S0360-1323(00)00059-7
  11. Dourish, P. (2001). Where the Action Is: The Foundations of Embodied Interaction. 1st ed., MIT Press, December 2001
  12. Dubberly, H (2001). Alan Cooper and the Goal Directed Design Process. Gain AIGA Journal of Design for the Network Economy, 1(2), Retrieved Mar. 2016 from http://www.dubberly.com/articles/alan-cooper-and-the-goaldirected-design-process.html
  13. Dunovska, T., Drkal F., & Hensen, J. (1999). Barriers and solutions to the use of building simulation in the czech republic. Proceedings of the 6th International Conference on Building Simulation, Kyoto, Japan, Sep.13-15, 257-262
  14. Gartner (2016). Gartner's 2016 Hype Cycle for Emerging Technologies Identifies Three Key Trends That Organizations Must Track to Gain Competitive Advantage, Gartner, Inc. Retrieved Mar. 2016 from http://www.gartner.com/newsroom/id/3412017
  15. Hamilton, I. G., Summerfield, A. J., Lowe, R., Ruyssevelt, P., Elwell, C. A., & Oreszczyn, T. (2013). Energy epidemiology: a new approach to end-use energy demand research. Building Research & Information, 41, 482-497 https://doi.org/10.1080/09613218.2013.798142
  16. Hens, H. (2013). Actual limits of HAM-modelling looking at problems encountered in practice, Part2: examples showing modelling limitations. ibpsaNEWS, 23(1), 23-33
  17. Hensen, J., & Lamberts, R. (2011). Building performance simulation for design and operation: Introduction to building performance simulation. 1st ed., Spon Press
  18. Heo, Y. Choudhary, R. & Augenbroe, G. (2012). Calibration of building energy models for retrofit analysis under uncertainty. Energy and Buildings, 47, 550-560 https://doi.org/10.1016/j.enbuild.2011.12.029
  19. Heo, Y, Augenbroe, G., Graziano, D., Muehleisen, R.T., & Guzowski, L. (2015). Scalable methodology for large scale building energy improvement: Relevance of calibration in model-based retrofit analysis. Building and Environment, 87, 342-350 https://doi.org/10.1016/j.buildenv.2014.12.016
  20. Hien, W.N., Poh, L.K., & Feriadi, H. (2003). Computer-based performance simulation for building design and evaluation: The Singapore perspective, Simulation and Gaming, 34(2), 457-477 https://doi.org/10.1177/1046878103255917
  21. Hopfe, C.J., & Hensen, J.L.M (2011). Uncertainty analysis in building performance simulation for design support. Energy and Buildings, 43(10), 2798-2805 https://doi.org/10.1016/j.enbuild.2011.06.034
  22. IBPSA (2013). The newsletter of the International Building Performance Simulation Association letter. International Building Performance Simulation Association: Interview with Yeonsook Heo IBPSA Young Contributor award winner, 23(2), Oct. 2013
  23. IBPSA (1987-2015). Proceedings of the International Building Performance Simulation Association (IBPSA) conference ('87. '91, '93, '95, '97, '99, '01, '03, '05, '07, '09, '11, '13, '15)
  24. Ishii H., & Ullmer, B. (1997). Tangible Bits: Towards Seamless Interfaces between People, Bits and Atoms. Proceedings of CHI '97, March 22-27
  25. John, D.S (1991), A skeptic's guide to computer models. In Barney, G.O. et al. (eds.), Managing a Nation: The Microcomputer Software Catalog. Boulder, CO: Westview Press, 209-229
  26. Kim, D.W., & Park, C.S. (2012). Needs and issues for better use of building energy simulation tools at design stage, Journal of the Architectural Institute of Korea, Planning and Design, 28(10), 317-325 https://doi.org/10.5659/JAIK_PD.2012.28.10.317
  27. Kim, D.W., Suh, W.J., Jung, J.T., Yoon, S.H., & Park, C.S. (2012). A Mini Test-bed for Modeling, Simulation and Calibration, Proceedings of the 2nd International Conference on Building Energy and Environment 2012, August 1-4, Boulder, Colorado, USA, 1145-1152
  28. Macdonald, I., & Strachan, P. (2001). Practical application of uncertainty analysis, Energy and Buildings, 33(3), 219-227 https://doi.org/10.1016/S0378-7788(00)00085-2
  29. Mahdavi (1998). Computational decision support and the building delivery process: a necessary dialogue, Automation in Construction, 7(2-3), 205-211 https://doi.org/10.1016/S0926-5805(97)00061-7
  30. Mahdavi, A., Feuer, S., Redlein, A., & Suter, G. (2003). An inquiry into the building performance simulation tools usage by architects in austria, Proceedings of the 8th International Conference on Building Simulation, Eindhoven, Netherlands, August 11-14, 777-784
  31. Malkawi A., & Augenbroe, G. (2003). Advanced building simulation - ch.2. Uncertainty in building simulation, 1st ed., Spon Press, New York
  32. Pallasmaa, J. (2005). The eyes of the skin: architecture and the senses, 2nd ed., John Wiley & Sons Ltd
  33. Pallasmaa, J. (2009). Thinking Hand: Existential and Embodied Wisdom in Architecture, 1st ed., Willey 2009
  34. Prazeres, L., & J. Clarke. (2005). Qualitative Analysis on the Usefulness of Perceptualization Techniques in Communicating Building Simulation Outputs, Proceeding of the 9th International Conference on Building Simulation, Montreal, Canada, August 15-18, 961-968
  35. Rao, S.S. (1996). Engineering optimization - theory and practice. 3rd Ed., John Wiley & Sons, Inc.
  36. Reddy, T.A. (2006). Literature review on calibration of building energy simulation programs: uses, problems, procedures, uncertainty, and tools, ASHRAE transactions, 112(2), 226-240
  37. Reddy, T.A., Maor, I., & Panjapornpon, C. (2007). Calibrating detailed building energy simulation programs with measured data part II: application to three case study office buildings (RP-iosi), HVAC&R Research, 13(2), 243-265 https://doi.org/10.1080/10789669.2007.10390953
  38. de Souza, C.B. (2012). Contrasting paradigms of design thinking: the building thermal simulation tool user vs. the building designer, Automation in Construction, 22, 112-122 https://doi.org/10.1016/j.autcon.2011.09.008
  39. de Souza, C.B. (2013). Studies into the use of building thermal physics to inform design decision making, Automation in construction, 30, 81-93 https://doi.org/10.1016/j.autcon.2012.11.026
  40. de Souza, C.B., & Tucker, S. (2016). Thermal simulation software outputs: a conceptual data model of information presentation for building design decision-making, Journal of Building Performance Simulation, 9(3), 227-254 https://doi.org/10.1080/19401493.2015.1030450
  41. Thomas, V.C. (2006). Using M-E Design Programs (some reasons for the lack of progress), Presentation at IBPSA-USA Winter Meeting 2006, Chicago, IL
  42. Trcka, M., & Hensen, J.L.M. (2010). Overview of HVAC system simulation, Automation in Construction, 19(2), 93-99 https://doi.org/10.1016/j.autcon.2009.11.019
  43. Wetter, M., Bonvini, M., & Nouidui, T.S. (2016). Equation-based languages-A new paradigm for building energy modeling, simulation and optimization. Energy and Buildings, 117, 290-300. https://doi.org/10.1016/j.enbuild.2015.10.017
  44. de Wilde, P., & Prickett, D. (2009). Preconditions for the use of simulation in M&E engineering, Proceedings of the 8th International Conference on Building Simulation, Glasgow, Scotland, July 27-30, 414-419
  45. 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