Uncertainty quantification of microclimate variables in building energy models

Yuming Sun, Yeonsook Heo, Matthias Tan, Huizhi Xie, C. F. Jeff Wu, Godfried Augenbroe

Research output: Contribution to journalArticlepeer-review

68 Citations (Scopus)


The last decade has seen a surge in the need for uncertainty analysis (UA) for building energy assessment. The rigorous determination of uncertainty in model parameters is a vital but often overlooked part of UA. To undertake this, one has to turn one's attention to a thriving area in engineering statistics that focuses on uncertainty quantification (UQ) for short. This paper applies dedicated methods and theories that are emerging in this area of statistics to the field of building energy models, and specifically to the microclimate variables embedded in them. We argue that knowing the uncertainty in these variables is a vital prerequisite for ensuing UA of whole building behaviour. Indeed, significant discrepancies have been observed between the predicted and measured state variables of building microclimates. This paper uses a set of approaches from the growing UQ arsenal, mostly regression-based methods, to develop statistical models that quantify the uncertainties in the following most significant microclimate variables: local temperature, wind speed, wind pressure and solar irradiation. These are the microclimate variables used by building energy models to define boundary conditions that encapsulate the interaction of the building with the surrounding physical environment. Although our analysis is generically applicable to any of the current energy models, we will base our UQ examples on the energy model used in EnergyPlus.

Original languageEnglish
Pages (from-to)17-32
Number of pages16
JournalJournal of Building Performance Simulation
Issue number1
Publication statusPublished - 2014 Jan
Externally publishedYes

Bibliographical note

Funding Information:
This research was funded through NSF-EFRI-SEED Award 1038248, ‘Risk Conscious Design and Retrofit of Buildings for Low Energy’, 2010–2014, (PI: Godfried Augenbroe, Georgia Institute of Technology)


  • building energy models
  • microclimate variables
  • uncertainty analysis
  • uncertainty quantification
  • urban environment

ASJC Scopus subject areas

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


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