Inferring the thermal resistance and effective thermal mass distribution of a wall from in situ measurements to characterise heat transfer at both the interior and exterior surfaces

Gori, Virginia; Marincioni, Valentina; Biddulph, P.; Elwell, Clifford A.

doi:10.1016/j.enbuild.2016.10.043

Cited by 48 publications

(54 citation statements)

References 25 publications

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“…The heat flow for the 1TM model without solar radiation is obtained from Equation (2) by setting R 3 to zero [29,30]. While either surface or air temperature can be used on the internal side for both models, surface temperature can only be used on the external side when solar radiation is not accounted for separately.…”

Section: Dynamic Model: Lumped-thermal-mass Modelsmentioning

confidence: 99%

“…Either the internal heat flux data stream, the external one, or both simultaneously (Equation (2)) can be optimised during model fitting [29].…”

Section: Likelihood Functionmentioning

confidence: 99%

“…These models were used to predict the time series of interest (i.e., either the heat flux through the structure or the temperatures on opposite sides) and evaluate the thermophysical properties of the element by means of Bayesian inference (Section 2.3) [6,29,30]. Note that in this paper, the term "predict" is used in its statistical meaning of estimating the value of a random variable rather than as a synonym of forecast, which implies making a prediction of the value of the variable at a given time in the future.…”

Section: Dynamic Model: Lumped-thermal-mass Modelsmentioning

confidence: 99%

“…Lumped-thermal-mass models including between zero and four effective thermal masses-both with and without the explicit incorporation of solar radiation-were developed in this research (refer to [6] for a pure resistive model, to [29] for a model including two effective thermal masses and four thermal resistances without the explicit incorporation of solar radiation (i.e., two thermal mass model, 2TM), and to [30] for all other models). These models may provide a better description of the thermal structure of the building element, such as in the case of more complex structures.…”

Section: Dynamic Model: Lumped-thermal-mass Modelsmentioning

confidence: 99%

“…Under the iid assumption, the residuals of each data stream contributing to the fit (i.e., the heat flux(es) in this application) can be modelled as an additive white Gaussian noise [29,38], and their probability density function is a multivariate Gaussian distribution (N ) with zero mean and diagonal covariance matrix…”

Section: Independent and Identically Distributed Residualsmentioning

confidence: 99%

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A Bayesian Dynamic Method to Estimate the Thermophysical Properties of Building Elements in All Seasons, Orientations and with Reduced Error

Gori,

Biddulph,

Elwell

2018

Energies

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Abstract:The performance gap between the expected and actual energy performance of buildings and elements has stimulated interest in in-situ measurements. Most research has employed quasi-static analysis methods that estimate heat loss metrics such as U-values, without taking advantage of the rich time series data that is often recorded. This paper presents a dynamic Bayesian-based method to estimate the thermophysical properties of building elements from in-situ measurements. The analysis includes Markov chain Monte Carlo (MCMC) estimation, priors, uncertainty analysis, and model comparison to select the most appropriate model. Data from two case study dwellings is used to illustrate model performance; U-value estimates from the dynamic and static methods are within error estimates, with the dynamic model generally requiring much shorter time series than the static model. The dynamic model produced robust results at all times of year, including when the average indoor-to-outdoor temperature difference was low, when external temperatures had large daily variation, and measurements were subjected to direct solar radiation. Further, the probability distributions of parameters may provide insights into the thermal performance of elements. Dynamic methods such as that presented herein may enable wider characterisation of the performance of building elements as built, supporting work to reduce the performance gap.

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Section: Dynamic Model: Lumped-thermal-mass Modelsmentioning

confidence: 99%

“…Either the internal heat flux data stream, the external one, or both simultaneously (Equation (2)) can be optimised during model fitting [29].…”

Section: Likelihood Functionmentioning

confidence: 99%

Section: Dynamic Model: Lumped-thermal-mass Modelsmentioning

confidence: 99%

Section: Dynamic Model: Lumped-thermal-mass Modelsmentioning

confidence: 99%

Section: Independent and Identically Distributed Residualsmentioning

confidence: 99%

See 3 more Smart Citations

A Bayesian Dynamic Method to Estimate the Thermophysical Properties of Building Elements in All Seasons, Orientations and with Reduced Error

Gori,

Biddulph,

Elwell

2018

Energies

Self Cite

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Thermal Insulation for Fixed Formwork from Expanded-Clay Concrete Blocks

Proskurovskis

Nazinyan

Tarasova

et al. 2020

Proceedings of EECE 2019

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The impact of thermal mass on building energy consumption

2017

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This paper presents new metrics to measure the effect of thermal mass on the energy required to heat and cool buildings. Previous studies have been flawed as they have not considered the interaction between intermittent occupancy and thermal mass, which has a significant impact on overall energy use. However, existing parameters do not adequately capture these effects, so the new metrics developed in this paper are used to analyse the impact of thermal mass in hot climates with active cooling, and cold climates with active heating. The results agree with existing literature that high thermal mass structures are likely to be effective in hot climates; however, in cold climates the drawbacks of high thermal mass likely outweigh the advantages, and high thermal mass can cause an increase in energy use. This finding has implications for the design of buildings in cold climates, and contradicts the commonly-held assumption that high thermal mass is correlated with low energy use. The new metrics (transient energy ratio and effective U-value) provide a generalisable method to quantify these effects. They are further used here to analyse the dynamic performance of heavily insulated buildings and show that high thermal mass often leads to higher energy use in cold climates.

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Inferring the thermal resistance and effective thermal mass distribution of a wall from in situ measurements to characterise heat transfer at both the interior and exterior surfaces

Cited by 48 publications

References 25 publications

A Bayesian Dynamic Method to Estimate the Thermophysical Properties of Building Elements in All Seasons, Orientations and with Reduced Error

A Bayesian Dynamic Method to Estimate the Thermophysical Properties of Building Elements in All Seasons, Orientations and with Reduced Error

Thermal Insulation for Fixed Formwork from Expanded-Clay Concrete Blocks

The impact of thermal mass on building energy consumption

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