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Interface from BIM to BEM and its Application to Uncertainty Analysis in Windows
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 Title & Authors
Interface from BIM to BEM and its Application to Uncertainty Analysis in Windows
Kim, Young-Jin; Yi, Dong-Hyuk; Park, Cheol-Soo;
 
 Abstract
This paper addresses an interface from Building Information Model (BIM) to Building Energy Model (BEM). The interface converts an IFC file exported from BIM authoring tools (e.g. Revit, ArchiCAD) to an IDF file (EnergyPlus input file). For seamless data exchange, a set of mapping rules with regard to space boundary, thermal properties of construction materials & fenestration, vendor-specific information, etc. were introduced. The interface was developed for (1) data sharing and data reuse between architects and simulationists, (2) quick and easy performance assessment during the design process, (3) minimizing experts' intervention and efforts. It is noteworthy that the interface can provide a features of Uncertainty and Sensitivity Analysis (UA, SA). In the paper, a case study of an office building is presented using the BIM-to-BEM interface.
 Keywords
Building Information Model (BIM);Building Energy Model (BEM);Interoperability;Interface;Building simulation;
 Language
Korean
 Cited by
 References
1.
AIA. (2008). Document E202-2008 Building Information Modeling Protocol Exhibit. American Institute of Architects.

2.
ASHRAE. (2004). ASHRAE STANDARD, Ventilation for Acceptable Indoor Air Quality, ANSI/ASHRAE/IESNA Standard 62.1-2004.

3.
ASHRAE. (2013). ASHRAE Handbook Fundamentals. Atlanta: American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc.

4.
Augenbroe, G. (2001). Building simulation trends going into the new millenium, Keynote, Proceedings of the 7th IBPSA Conference, August, 13-15, Rio de Janeiro, Brazil, 15-27.

5.
Augenbroe, G. (2002). Trends in building simulation, Building and Environment, 37, 891-902. crossref(new window)

6.
Augenbroe, G., & Hensen, J. (2004). Simulation for better building design, Building and Environment, 38, 875-877.

7.
Bazjanac, V. (2008). IFC BIM-Based Methodology for Semi-Automated Building Energy Performance Simulation, Lawrence Berkley National Laboratory (LBNL), 919E.

8.
Bogenstatter, U. (2000). Prediction and Optimization of life-cycle costs in early design, Building Research & Information, 28, 376-386. crossref(new window)

9.
BuildingSMART. (2012). http://www.buildingsmart.org/, last reviewed 06/25/2012.

10.
Clarke, J. A. (2001). Energy simulation in building design. 2nd ed. Oxford: Butterworth-Heinemann (1st edition 1985).

11.
CIBSE. (1998). Building Energy and Environment Modelling, CIBSE Application Manual AM 11.

12.
Choo, S. Y., Lee, K. H., & Park, S. K. (2012). A Study on LOD(Level of Development) for Development of Green BIM Guidelines, Focused on Energy Performance Estimation. Journal of the Architectural Institute of Korea, Planning and Design Section, 28(6), 37-47.

13.
de Wilde, P., & van der Voorden, M. (2003). Computational support for the selection of energy saving building components. In: Schellen and van der Spoel, eds. Proceedings of the 8th IBPSA Conference, August 11-14, Eindhoven, Netherlands, 1409-1416.

14.
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. crossref(new window)

15.
de Wit, S. (2001). Uncertainty in prediction of thermal comfort in Buildings, Ph.D. thesis, Tu Delft Netherlands.

16.
Dijk, H., & Spiekman, M. (2004). Energy Performance of Buildings; Outline for Harmonised EP Procedures. Final report EU SAVE ENPER project, Task B6. TNO Building and Construction Research, Delft(NL), June 29, 2004 (http://www.enper.org).

17.
DOE. (2010a). Building Energy Software Tools Directory [online]. Available from: http://apps1.eere.energy.gov/buildings/tools_directory/ Assessed 1 October 2010.

18.
DOE. (2010b). EnergyPlus 6.0 Input/Output Reference: The Encyclopedic Reference to EnergyPlus Input and Output, US Department Of Energy.

19.
Donn, M. R. (1999). Quality assurance: simulation and the real world. In: Kakahara, Yoshida, Udagawa, and Hensen, eds., Proceedings of the 6th IBPSA Conference, September 13-15, Kyoto, Japan, 1139-1146.

20.
Fowler, K. M., & Rauch, E. M. (2006). Sustainable Building Rating Systems Summary, Pacific Northwest National Laboratory.

21.
gbXML. (2012). http://www.gbXML.org/. last reviewed, 06/25/2012.

22.
Hand, W. J. (1998). Removing barriers to the use of simulation in the building design professions, Ph.D. thesis, University of Strathclyde, Department of Mechanical Engineering, UK.

23.
Hensen, J. (2004). Towards more effective use of building performance simulation in design, Developments in Design & Decision Support Systems in Architecture and Urban Planning, edited by Jos P. van Leeuwen and Harry J. P. Timmermans, Eindhoven University of Technology, Department of Architecture, Building and Planning, Eindhoven, the Netherlands.

24.
Hopfe, C. J. (2009). Uncertainty and sensitivity analysis in building performance simulation for decision support and design optimization. Ph.D thesis, Technische Universiteit Eindhoven.

25.
Judkoff, R., & Neymark, J. (1995). International Energy Agency Building Energy Simulation Test (BESTEST) and Diagnostic Method, IEA Energy Conservation in Buildings and Community Systems Programme Annex 21 Subtask C and IEA Solar Heating and Cooling Programme Task 12 Subtask B.

26.
Judkoff, R., & Neymark, J. (2006). Model validation and testing: The methodological foundation of ASHRAE Standard 140, ASHRAE Transaction, 112, 367-376.

27.
Kim, Y. J., Oh, S. M., Park, C. S., & Kim, I. H. (2011). Interoperability and Uncertainty in BIM-based Building Energy Performance Assessment. Journal of the Architectural Institute of Korea, Planning and Design Section, 27(6), 247-255.

28.
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.

29.
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. crossref(new window)

30.
Keilholz, W., Ferries, B., Andrieux, F., & Noel, J. (2009). A simple, neutral building data model. In Zarli, A. & Scherer, R. J. (eds.) "eWork and eBusiness in Architecture, Engineering and Construction", Proceedings of the 7th European conference on product and process modelling, Sophia Antipolis, 10-12 September 2008, CRC Press/Balkema, 105-109.

31.
Laret, L., Liebecq, G., & Ngendakumana, P. (1987). Building. IEA Annex 10 Report, University of Liege, Belgium.

32.
Lechner, N. (2001). Heating, Cooling, Lighting-Design Methods for Architects, John Wiley and Sons.

33.
Macdonald, I. A., & Strachan, P. (2001). Practical application of uncertainty analysis, Energy and Buildings, 33, 219-227. crossref(new window)

34.
MacDonald, I. A. (2002). Quantifying the effects of uncertainty in building simulation, Ph.D. thesis, University of Strathclyde, Scotland.

35.
Maile, T., Fischer, M., & Bazjanac, V. (2007). Building energy performance simulation tools: a life-cycle and interoperable perspective, Stanford, California: Center for Integrated Facility Engineering.

36.
Mahdavi, A. (2003). Computational Building Models: Theme and Four Variations, Proceedings of the 8th IBPSA Conference, Aug.11-14, Eindhoven, Netherlands, 3-17.

37.
Mahdavi, A., Bachinger, J., & Suter, G. (2005). Toward a unified information space for the specification of building performance simulation results, Proceedings of the 9th IBPSA Conference, Aug. 15-18, Montreal, Canada, 671-676.

38.
Morbitzer, C. A. (2003). Towards the integration of simulation into the building design process. Ph.D. thesis. University of Strathclyde, Energy System Research Unit ESRU, UK.

39.
Park, C. S. (2006). Normative Assessment of Technical Building Performance. Journal of the Architectural Institute of Korea, Planning and Design Section, 22(11), 337-344.

40.
Prazeres, L., & Clarke, J. A. (2003). Communication Building Simulation Outputs to Users, Proceedings of the 8th IBPSA Conference, Aug.11-14, Eindhoven, Netherlands, 1053-1060.

41.
Soebarto, V. (2005). Teaching simulation programs in architecture schools: lessons learned. In: Beausoleil-Morrison and Bernier, eds., Proceedings of the 9th IBPSA Conference, Aug. 15-18, Montreal, Canada, 1147-1154.

42.
Wyss, G. D., & Gorgensen, K. H. (1998). A User's Guide to LHS: Sndia's Latin Hypercube Sampling Software, Albuquerque, NM, Sandia National Laboratories.