Uncertainty and sensitivity analysis for detailed design support

Christina Hopfe*, Jan Hensen, Wim Plokker

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


Nowadays, building performance simulation (BPS) is still primarily used for code compliance checking in the Netherlands whilst it could provide the user already useful design information by e.g. indicating design solutions or introducing uncertainty analysis (UA) and sensitivity analysis (SA). This paper summarizes results from an ongoing research introducing UA and SA in BPS. A case study is performed based on a hypothetical building which is part of an international test method for assessing the accuracy of BPS tools with respect to various building performance parameters. SA is accomplished via a freeware tool called Simlab. This is used as a pre- and postprocessor for the BPS software VA114. The SA is based on seven different input parameters, covering different categories like uncertainties in physical and design parameters as well as in boundary conditions. The sample matrix for the different input was generated with the Latin hypercube method. Results considering energy consumption (annual heating and cooling, peak loads) and thermal comfort (weight over- and underheating hours) are compared. The paper will finish with indicating how this research will be proceeded.

Original languageEnglish
Title of host publicationIBPSA 2007 - International Building Performance Simulation Association 2007
EditorsY Jiang, YX Zhu, XD Yang, XT Li
Number of pages6
Publication statusPublished - 2007
Externally publishedYes
EventBuilding Simulation 2007: BS 2007 - Beijing, China
Duration: 3 Sept 20076 Sept 2007


ConferenceBuilding Simulation 2007
Abbreviated titleBS 2007


  • Building performance
  • Energy consumption
  • Monte carlo analysis
  • Sensitivity analysis
  • Thermal comfort

ASJC Scopus subject areas

  • Computer Science Applications
  • Building and Construction
  • Architecture
  • Modelling and Simulation

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