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

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  • Virginia Gori

    (Physical Characterisation of Buildings Group, UCL Energy Institute, 14 Upper Woburn Place, London WC1H 0NN, UK)

  • Phillip Biddulph

    (Physical Characterisation of Buildings Group, UCL Energy Institute, 14 Upper Woburn Place, London WC1H 0NN, UK)

  • Clifford A. Elwell

    (Physical Characterisation of Buildings Group, UCL Energy Institute, 14 Upper Woburn Place, London WC1H 0NN, UK)

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.

Suggested Citation

  • Virginia Gori & Phillip Biddulph & Clifford A. Elwell, 2018. "A Bayesian Dynamic Method to Estimate the Thermophysical Properties of Building Elements in All Seasons, Orientations and with Reduced Error," Energies, MDPI, vol. 11(4), pages 1-27, March.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:4:p:802-:d:138881
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    References listed on IDEAS

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    1. Lehmann, B. & Ghazi Wakili, K. & Frank, Th. & Vera Collado, B. & Tanner, Ch., 2013. "Effects of individual climatic parameters on the infrared thermography of buildings," Applied Energy, Elsevier, vol. 110(C), pages 29-43.
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    Cited by:

    1. Yuiko Sakuma & Hiroaki Nishi, 2019. "Estimation of Building Thermal Performance using Simple Sensors and Air Conditioners," Energies, MDPI, vol. 12(15), pages 1-22, July.
    2. Lihui Zhang & Zhenzhen Chen & Donghui Wen & Xudong Wang & Daqian Zhang & Jun Liang, 2018. "Estimation of the Time-Varying High-Intensity Heat Flux for a Two-Layer Hollow Cylinder," Energies, MDPI, vol. 11(12), pages 1-16, November.
    3. Juricic, Sarah & Goffart, Jeanne & Rouchier, Simon & Foucquier, Aurélie & Cellier, Nicolas & Fraisse, Gilles, 2021. "Influence of natural weather variability on the thermal characterisation of a building envelope," Applied Energy, Elsevier, vol. 288(C).
    4. Valentina Marincioni & Virginia Gori & Ernst Jan de Place Hansen & Daniel Herrera-Avellanosa & Sara Mauri & Emanuela Giancola & Aitziber Egusquiza & Alessia Buda & Eleonora Leonardi & Alexander Rieser, 2021. "How Can Scientific Literature Support Decision-Making in the Renovation of Historic Buildings? An Evidence-Based Approach for Improving the Performance of Walls," Sustainability, MDPI, vol. 13(4), pages 1-20, February.

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