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Influence of natural weather variability on the thermal characterisation of a building envelope

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  • Juricic, Sarah
  • Goffart, Jeanne
  • Rouchier, Simon
  • Foucquier, Aurélie
  • Cellier, Nicolas
  • Fraisse, Gilles

Abstract

The thermal characterisation of a building envelope is usually best performed from on-site measurements with optimised controlled indoor conditions. Conversely, occupant-friendly measurement conditions provide less informative data. Notwithstanding occupancy, the boundary conditions alone contribute to a greater extent to the energy balance, which implies that non-intrusive conditions bring into question the reproducibility and relevance of such measurement. This paper proposes an original numerical methodology to assess the reproducibility and accuracy of the estimation of the overall thermal resistance of an envelope under variable weather conditions. A comprehensive building energy model serves as reference model to produce synthetic data mimicking non-intrusive conditions, each with a different weather dataset. An appropriate model is calibrated from the synthetic data and provides a thermal resistance estimate. The accuracy of the estimates is then assessed in light of the particular weather conditions used for data generation. The originality also lies in the set of weather data that allow for uncertainty and global sensitivity analyses of all estimates with respect to six weather variables. The methodology is applied to a one-storey house reference model, for which thermal resistance is inferred from calibrated RC models. Robust estimations are achieved within 11 days. The outdoor temperature and the wind speed are highly influential because of the large air change rate in the case study.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:appene:v:288:y:2021:i:c:s0306261921001252
    DOI: 10.1016/j.apenergy.2021.116582
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    References listed on IDEAS

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    1. 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.
    2. Petojević, Zorana & Gospavić, Radovan & Todorović, Goran, 2018. "Estimation of thermal impulse response of a multi-layer building wall through in-situ experimental measurements in a dynamic regime with applications," Applied Energy, Elsevier, vol. 228(C), pages 468-486.
    3. Foucquier, Aurélie & Robert, Sylvain & Suard, Frédéric & Stéphan, Louis & Jay, Arnaud, 2013. "State of the art in building modelling and energy performances prediction: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 272-288.
    4. Marc C. Kennedy & Anthony O'Hagan, 2001. "Bayesian calibration of computer models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 425-464.
    5. Ramos Ruiz, Germán & Fernández Bandera, Carlos, 2017. "Analysis of uncertainty indices used for building envelope calibration," Applied Energy, Elsevier, vol. 185(P1), pages 82-94.
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    2. Shao, Junqiang & Huang, Zhiyuan & Chen, Yugui & Li, Depeng & Xu, Xiangguo, 2023. "A practical application-oriented model predictive control algorithm for direct expansion (DX) air-conditioning (A/C) systems that balances thermal comfort and energy consumption," Energy, Elsevier, vol. 269(C).

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