Probabilistic identification of inverse building model parameters

Gregory Pavlak, Anthony R. Florita, Gregor P. Henze, Balaji Rajagopalan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Probabilistic and nonlinear least squares parameter estimation methods are evaluated for inverse gray box model identification of a retail building. A detailed building energy simulation program is used to generate surrogate data for estimation of parameters. The most probable or optimal parameters from each method are compared through simulation of building zone temperature and thermal loads. The least squares method generally found solutions near probable regions of the posterior from the probabilistic approach, and simulation performance was very similar between best parameter sets. A brief overview of probabilistic estimation techniques is provided, along with potential improvements to the approach presented and brief discussion on its applicability for uncertainty quantification within the building science domain.

Original languageEnglish (US)
Title of host publicationAEI 2013
Subtitle of host publicationBuilding Solutions for Architectural Engineering - Proceedings of the 2013 Architectural Engineering National Conference
Pages255-264
Number of pages10
DOIs
StatePublished - Nov 15 2013
Event2013 Architectural Engineering National Conference: Building Solutions for Architectural Engineering, AEI 2013 - State College, PA, United States
Duration: Apr 3 2013Apr 5 2013

Publication series

NameAEI 2013: Building Solutions for Architectural Engineering - Proceedings of the 2013 Architectural Engineering National Conference

Other

Other2013 Architectural Engineering National Conference: Building Solutions for Architectural Engineering, AEI 2013
CountryUnited States
CityState College, PA
Period4/3/134/5/13

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Building and Construction
  • Architecture

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