Hostname: page-component-cd9895bd7-8ctnn Total loading time: 0 Render date: 2024-12-23T18:02:03.848Z Has data issue: false hasContentIssue false
  • English
  • Français

Assessment of uncertainty and confidence in building design exploration

Published online by Cambridge University Press:  07 October 2015

Roya Rezaee ,
Jason Brown ,
Godfried Augenbroe  and
Jinsol Kim
Roya Rezaee*
Affiliation:
High Performance Building, School of Architecture, Georgia Institute of Technology, Atlanta, Georgia, USA
Jason Brown
Affiliation:
High Performance Building, School of Architecture, Georgia Institute of Technology, Atlanta, Georgia, USA
Godfried Augenbroe
Affiliation:
High Performance Building, School of Architecture, Georgia Institute of Technology, Atlanta, Georgia, USA
Jinsol Kim
Affiliation:
High Performance Building, School of Architecture, Georgia Institute of Technology, Atlanta, Georgia, USA
*
Reprint requests to: Roya Rezaee, High Performance Building, School of Architecture, Georgia Institute of Technology, 247 4th Street, NM Suite 351, Atlanta, GA 30332-0155, USA; E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Performance assessment at early stages of buildings design is complicated by an inherent lack of information on the design and the uncertainty in how a building design may evolve to a final design. This pilot study reports on an initial quantification of such uncertainty associated with building energy performance and develops a method for informing decision makers of the risks in early design decisions under this uncertainty. Two case studies of building design decision situations under this uncertainty are explored along with using two different energy modeling tools: a reduced-order model and a high-order model. The intended contribution is to identify if a decision can be made with confidence in early design given a high level of uncertainty in the evolution of a design and what models can support decisions of this sort. Integration of the proposed decision support approach with a computer-aided design model is shown as well.

Keywords

Building Energy PerformanceConfidenceDesign Decision MakingDesign ExplorationUncertainty Analysis and Risk Assessment
Type
Special Issue Articles
Information
AI EDAM , Volume 29 , Special Issue 4: Generative and evolutionary design exploration , November 2015 , pp. 429 - 441
Copyright
Copyright © Cambridge University Press 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Attia, S., Beltrán, L., De Herde, A., & Hensen, J. (2009). “Architect friendly”: A comparison of ten different building performance simulation tools. Proc. 11th Int. Building Performance Simulation Association Conf., Glasgow, Scotland, July 27–30.Google Scholar
Augenbroe, G. (1992). Integrated building performance evaluation in the early design stages. Building and Environment 27(2), 149–161. doi:10.1016/0360-1323(92)90019-lCrossRefGoogle Scholar
Augenbroe, G. (2011). The role of simulation in performance based building. In Building Performance Simulation for Design and Operation (Jensen, J., & Lambert, R., Eds.). Abingdon: Spon Press.Google Scholar
Chong, Y.T., Chen, C.-H., & Leong, K.F. (2009). A heuristic-based approach to conceptual design. Research in Engineering Design 20(2), 97–116.CrossRefGoogle Scholar
Crawley, D.B., Lawrie, L.K., Winkelmann, F.C., & Pedersen, C.O. (2001). EnergyPlus: new capabilities in a whole-building energy simulation program. Proc. Int. Building Performance Simulation Conf., Rio de Janeiro, Brazil, August 13–15.Google Scholar
de Wit, M.S. (2001). Uncertainty in Predictions of Thermal Comfort in Buildings B2—Uncertainty in Predictions of Thermal Comfort in Buildings. Delft: Technische Universiteit Delft.Google Scholar
de Wit, M.S. (2003). Uncertainty in building simulation. In Advanced Building Simulation, p. 5. New York: Spon Press.Google Scholar
de Wit, S., & Augenbroe, G. (2002). Analysis of uncertainty in building design evaluations and its implications. Energy and Buildings 34(9), 951–958. doi:10.1016/s0378-7788(02)00070-1CrossRefGoogle Scholar
DOE. (2013). EnergyPlus Energy Simulation Software. http://apps1.eere.energy.gov/buildings/energyplus/Google Scholar
Domeshek, E.A., Herndon, M.F., Bennett, A.W., & Kolodner, J L. (1994). A case-based design aid for conceptual design of aircraft subsystems. Proc. 10th Conf. Artificial Intelligence for Applications, pp. 63–69. San Antonio, TX: IEEE.Google Scholar
EIA. (2006). 2003 Commercial Buildings Energy Consumption Survey (CBECS). Accessed at http://www.eia.doe.gov/emeu/cbecs/contents.htmlGoogle Scholar
Grew, B., Boussabaine, A.H., Kumar, B., & Topping, B.H.V. (1999). The use of rules of thumb and simple calculations for the checking of computer simulations of building structures. Proc. Computing Developments in Civil and Structural Engineering, pp. 9–12. Edinburgh: Civil-Comp Press.CrossRefGoogle Scholar
Hazelrigg, G.A. (2010). Fundamentals of decision making for engineering design and systems engineering. Unpublished manuscript.Google Scholar
Hensen, J., & Augenbroe, G. (2004). Performance simulation for better building design. Energy and Buildings 36(8), 735–736. doi:10.1016/j.enbuild.2004.06.004CrossRefGoogle Scholar
Hensen, J.L.M., & Lamberts, R. (2011). Introduction to building performance simulation. In Building Performance Simulation for Design and Operation. Abingdon: Spon Press.Google Scholar
Hopfe, C.J., & Hensen, J.L. (2011). Uncertainty analysis in building performance simulation for design support. Energy and Buildings 43(10), 2798–2805.CrossRefGoogle Scholar
Hopfe, C.J., Struck, C., Harputlugil, G.U., Hensen, J., & de Wilde, P. (2005). Exploration of the use of building performance simulation for conceptual design. Proc. IBPSA-NVL Conf. Delft: Technische Universiteit Delft.Google Scholar
ISO. (2008). ISO 13790:2008 Energy Performance of Buildings—Calculation of Energy Use for Space Heating and Cooling. Geneva: Author.Google Scholar
Kim, J., Augenbroe, G., & Suh, H.S. (2013). Comparative study of the LEED and ISO-CEN building energy performance rating methods. Proc. Int. Building Performance Simulation Association Conf., Chambéry, France, August 26–28.Google Scholar
Lee, B., Paredis, C., & Augenbroe, G. (2013). Towards better prediction of building performance: a workbench to analyze uncertainty in building simulation. Proc. Int. Building Performance Simulation Association Conf., Chambéry, France, August 26–28, 2013.CrossRefGoogle Scholar
Lee, S.H., Zhao, F., & Augenbroe, G. (2011). The use of normative energy calculation beyond building performance rating systems. Proc. 12th Int. Building Performance Simulation Association Conf., Sydney, November 14–16.Google Scholar
Malkawi, A., & Augenbroe, G. (2003). Advanced Building Simulation. New York: Spon Press.CrossRefGoogle Scholar
Okudan, G.E., & Tauhid, S. (2008). Concept selection methods—a literature review from 1980 to 2008. International Journal of Design Engineering 1(3), 243–277.CrossRefGoogle Scholar
PHX. (2013). PHX ModelCenter: Desktop Trade Studies. Accessed at http://www.phoenixint.com/software/phx-modelcenter.phpGoogle Scholar
Rezaee, R., Brown, J., Augenbroe, G., & Kim, J. (2014 a). Building energy performance estimation in early design decisions: quantification of uncertainty and assessment of confidence. Construction Research Congr., pp. 2195–2204, Atlanta, GA, May 19–21.Google Scholar
Rezaee, R., Brown, J., Augenbroe, G., & Kim, J. (2014 b). A new approach to the integration of energy assessment tools in CAD for early stage of design decision-making considering uncertainty. Proc. eWork and eBusiness in Architecture, Engineering and Construction: ECPPM 2014, p. 367. London: Taylor & Francis.CrossRefGoogle Scholar
Sanguinetti, P., Eastman, C., & Augenbroe, G. (2009). Courthouse energy evaluation: BIM and simulation model interoperability in concept design. Proc. 11th Int. Building Performance Simulation Association Conf., Glasgow, Scotland, July 27–30.Google Scholar
Struck, C., de Wilde, P.J.C.J., Hopfe, C.J., & Hensen, J.L.M. (2009). An investigation of the option space in conceptual building design for advanced building simulation. Advanced Engineering Informatics 23(4), 386–395. doi:10.1016/j.aei.2009.06.004CrossRefGoogle Scholar
Struck, C., & Hensen, J. (2007). On supporting design decision in conceptual design addressing specification uncertainties using performance simulation. Proc. 10th Int. Building Performance Simulation Association Conf., pp. 1434–1439. Beijing: Tsinghua University.Google Scholar
Struck, C., Hensen, J., & Kotek, P. (2009). On the application of uncertainty and sensitivity analysis with abstract building performance simulation tools. Journal of Building Physics 33, 5–27.CrossRefGoogle Scholar
Urban, B.J. (2007). The MIT Design Advisor: Simple and Rapid Energy Simulation of Early-Stage Building Designs. Cambridge, MA: MIT Press.Google Scholar
Vandezande, J., Krygiel, E., & Read, P. (2014). Introduction, Autodesk Revit Architecture 2014. Hoboken, NJ: Sybex Essentials.Google Scholar
Zhao, F. (2012). Agent-based modeling of commercial buildings stocks for energy policy and demand response analysis. PhD Thesis. Georgia Instiute of Technology.Google Scholar