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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]
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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 

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