Bayesian inference for estimating thermal properties of a historic building wall

Berger, Julien; Orlande, Helcio R. B.; Mendes, Nathan; Guernouti, Sihem

doi:10.1016/j.buildenv.2016.06.037

Cited by 48 publications

(34 citation statements)

References 36 publications

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“…An example of this was done by [6] in a problem of one-dimensional heat transfer. However, a necessary condition for a successful parameter estimation is that the model used for inference accurately represents the complexity of the physical system, and sensor information is sufficient to capture it.…”

Section: Resultsmentioning

confidence: 99%

“…Solving inverse heat transfer problems in the Bayesian framework is fairly recent [21,38], but already has several interesting examples in the building physics field. These applications mainly fall within two categories: the calibration of building energy models [40,20], and the characterisation of thermal properties of materials and components [7,6].…”

Section: Principlementioning

confidence: 99%

“…They are not quite as widespread as in other fields but are progressively gaining interest because of their increasing accessibility to non-mathematicians. Possible applications may be, among others: inverse modelling of multi-zone buildings [11,22]; in-situ diagnostics of thermal properties [8,7,6]; model selection and calibration for thermal retrofit investigation [20].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Hygric characterization of wood fiber insulation under uncertainty with dynamic measurements and Markov Chain Monte-Carlo algorithm

Rouchier

Busser

Pailha

et al. 2017

Building and Environment

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The present work is the hygric characterization of wood fibre insulation boards, using dynamic measurements of relative humidity and sample weight, analyzed in the frame of Bayesian inference for parameter identification under uncertainty. It is an attempt at identifying detailed profiles of moisture-dependent properties, and thus a relatively high number of parameters. Because of this ambition, some caution should be exercised once the outcome of the inversion algorithm is available: in addition to confidence intervals of parameters provided by the Bayesian framework, a simplified form of identifiability analysis is performed by analysing a posteriori parameter correlations and likelihood-based confidence intervals.The characterization methodology does not require for the model structure to have a differentiable analytical formulation, or for material samples to reach mass equilibrium between each RH step of the experimental process. Two separate experimental designs were used for material characterization and for validation, respectively. Results show a clear relation between available information (experimental data) and inference (confidence intervals of parameters). A single relative humidity step is not informative enough for a precise inference of moisture-dependent properties such as vapour permeability and moisture capacity. A two-step experiment however holds enough information to significantly reduce parameter uncertainty.

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Section: Resultsmentioning

confidence: 99%

Section: Principlementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hygric characterization of wood fiber insulation under uncertainty with dynamic measurements and Markov Chain Monte-Carlo algorithm

Rouchier

Busser

Pailha

et al. 2017

Building and Environment

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“…The Bayesian calibration procedure has been used in the individual building analysis for calibration of unknown input (Heo et al 2013(Heo et al , 2015aLi et al 2015aLi et al , 2016Kang and Krarti 2016;Berger et al 2016), retrofit analysis (Heo et al 2013(Heo et al , 2015a, comparison with traditional calibration method (Pavlak et al 2013), use of simplified model (Kim et al 2013;Pavlak et al 2013), influence of uncertainties in the input data (Heo et al 2015b), determination of informative energy data (Tian et al 2016), and meta-model comparison (Kim 2016;Li et al 2016). Fig.…”

Section: Stochastic Building Energy Models For Individual Buildingsmentioning

confidence: 99%

Review on stochastic modeling methods for building stock energy prediction

Lim

Zhai

2017

Build. Simul.

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Increasing risks of energy security and greenhouse gas emission due to the growing urbanization trend have prompted the need for urban energy demand prediction and management, in which the building energy consumption is the main cause. This paper reviews the recent advances and state-of-the-art in modeling building stock energy consumption, including both the top-down and bottom-up approaches. The study compares and summarizes the strengths and weaknesses of each primary method. Specific focus has been paid to the bottom-up stochastic engineering modeling methods, which hold sound quantitative theory bases as well as considering uncertain reality conditions. Stochastic building stock energy models account for the uncertainties that are the main limitation in existing building stock models. Discussions are provided regarding the process in the current stochastic building stock energy model. Challenges and possible future directions are examined for the improvement of stochastic building stock energy model.

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“…The paper exposes the determination thermotechnical uniformity coefficient for the basement floor over steel beams with brick vaults filling. The result of this study can be used for designing the insulation of basement floors in historic buildings [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Heat transfer performance uniformity factor for the basement floor made of brick vaults in historic buildings

Murgul

2017

MATEC Web Conf.

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Abstract. The paper exposes the calculation of Heat transfer performance uniformity factor for the basement floor made of brick vaults in residential historic buildings. It was determined that the temperature pattern on the floor surface of the premise could be characterized as uniform one. Heat transfer performance uniformity factor for the considered basement floor design can be ignored during the thermotechnical calculations. Thermal resistance calculation is performed for the overlap structure with the smallest thickness.

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Bayesian inference for estimating thermal properties of a historic building wall

Cited by 48 publications

References 36 publications

Hygric characterization of wood fiber insulation under uncertainty with dynamic measurements and Markov Chain Monte-Carlo algorithm

Hygric characterization of wood fiber insulation under uncertainty with dynamic measurements and Markov Chain Monte-Carlo algorithm

Review on stochastic modeling methods for building stock energy prediction

Heat transfer performance uniformity factor for the basement floor made of brick vaults in historic buildings

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