Sensitivity analysis methods for building energy models: Comparing computational costs and extractable information

Kathrin Menberg, Yeonsook Heo, Ruchi Choudhary

Research output: Contribution to journalArticlepeer-review

130 Citations (Scopus)


Though sensitivity analysis has been widely applied in the context of building energy models (BEMs), there are few studies that investigate the performance of different sensitivity analysis methods in relation to dynamic, high-order, non-linear behaviour and the level of uncertainty in building energy models. We scrutinise three distinctive sensitivity analysis methods: (a) the computationally efficient Morris method for parameter screening, (b) linear regression analysis (medium computational costs) and (c) Sobol method (high computational costs). It is revealed that the results from Morris method taking the commonly used measure for parameter influence can be unstable, while using the median value yields robust results for evaluations with small sample sizes. For the dominant parameters the results from all three sensitivity analysis methods are in very good agreement. Regarding the evaluation of parameter ranking or the differentiation of influential and negligible parameters, the computationally costly quantitative methods provide the same information for the model in this study as the computational efficient Morris method using the median value. Exploring different methods to investigate higher-order effects and parameter interactions, reveals that correlation of elementary effects and parameter values in Morris method can also provide basic information about parameter interactions.

Original languageEnglish
Pages (from-to)433-445
Number of pages13
JournalEnergy and Buildings
Publication statusPublished - 2016 Dec 1
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2016 The Authors


  • Building energy model
  • Morris method
  • Regression analysis
  • Sensitivity analysis
  • Sobol method

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Mechanical Engineering
  • Electrical and Electronic Engineering


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