IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v288y2021ics0306261921001252.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261921001252
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2021.116582?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ozarisoy, B. & Altan, H., 2022. "Significance of occupancy patterns and habitual household adaptive behaviour on home-energy performance of post-war social-housing estate in the South-eastern Mediterranean climate: Energy policy desi," Energy, Elsevier, vol. 244(PB).
    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Eva Lucas Segarra & Germán Ramos Ruiz & Vicente Gutiérrez González & Antonis Peppas & Carlos Fernández Bandera, 2020. "Impact Assessment for Building Energy Models Using Observed vs. Third-Party Weather Data Sets," Sustainability, MDPI, vol. 12(17), pages 1-27, August.
    2. Eva Lucas Segarra & Hu Du & Germán Ramos Ruiz & Carlos Fernández Bandera, 2019. "Methodology for the Quantification of the Impact of Weather Forecasts in Predictive Simulation Models," Energies, MDPI, vol. 12(7), pages 1-16, April.
    3. Gatt, Damien & Yousif, Charles & Cellura, Maurizio & Camilleri, Liberato & Guarino, Francesco, 2020. "Assessment of building energy modelling studies to meet the requirements of the new Energy Performance of Buildings Directive," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    4. Fumo, Nelson, 2014. "A review on the basics of building energy estimation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 53-60.
    5. Matthias Katzfuss & Joseph Guinness & Wenlong Gong & Daniel Zilber, 2020. "Vecchia Approximations of Gaussian-Process Predictions," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(3), pages 383-414, September.
    6. Vallianos, Charalampos & Candanedo, José & Athienitis, Andreas, 2023. "Application of a large smart thermostat dataset for model calibration and Model Predictive Control implementation in the residential sector," Energy, Elsevier, vol. 278(PA).
    7. Hao Wu & Michael Browne, 2015. "Random Model Discrepancy: Interpretations and Technicalities (A Rejoinder)," Psychometrika, Springer;The Psychometric Society, vol. 80(3), pages 619-624, September.
    8. Anna Kipping & Erik Trømborg, 2017. "Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock," Energies, MDPI, vol. 11(1), pages 1-20, December.
    9. Solène Goy & François Maréchal & Donal Finn, 2020. "Data for Urban Scale Building Energy Modelling: Assessing Impacts and Overcoming Availability Challenges," Energies, MDPI, vol. 13(16), pages 1-23, August.
    10. Gokhale, Gargya & Claessens, Bert & Develder, Chris, 2022. "Physics informed neural networks for control oriented thermal modeling of buildings," Applied Energy, Elsevier, vol. 314(C).
    11. Chen, Hanfei & Lin, ChihChieh & Longtin, Jon P., 2019. "Dynamic modeling and parameter optimization of a free-piston Vuilleumier heat pump with dwell-based motion," Applied Energy, Elsevier, vol. 242(C), pages 741-751.
    12. Xiaoyu Xiong & Benjamin D. Youngman & Theodoros Economou, 2021. "Data fusion with Gaussian processes for estimation of environmental hazard events," Environmetrics, John Wiley & Sons, Ltd., vol. 32(3), May.
    13. Petropoulos, G. & Wooster, M.J. & Carlson, T.N. & Kennedy, M.C. & Scholze, M., 2009. "A global Bayesian sensitivity analysis of the 1d SimSphere soil–vegetation–atmospheric transfer (SVAT) model using Gaussian model emulation," Ecological Modelling, Elsevier, vol. 220(19), pages 2427-2440.
    14. Tomasz Szul & Krzysztof Nęcka & Stanisław Lis, 2021. "Application of the Takagi-Sugeno Fuzzy Modeling to Forecast Energy Efficiency in Real Buildings Undergoing Thermal Improvement," Energies, MDPI, vol. 14(7), pages 1-16, March.
    15. Drignei, Dorin, 2011. "A general statistical model for computer experiments with time series output," Reliability Engineering and System Safety, Elsevier, vol. 96(4), pages 460-467.
    16. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A three-stage optimization methodology for envelope design of passive house considering energy demand, thermal comfort and cost," Energy, Elsevier, vol. 192(C).
    17. Yuan, Jun & Ng, Szu Hui, 2013. "A sequential approach for stochastic computer model calibration and prediction," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 273-286.
    18. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    19. Gautham Krishnadas & Aristides Kiprakis, 2020. "A Machine Learning Pipeline for Demand Response Capacity Scheduling," Energies, MDPI, vol. 13(7), pages 1-25, April.
    20. Gianluca Serale & Massimo Fiorentini & Alfonso Capozzoli & Daniele Bernardini & Alberto Bemporad, 2018. "Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities," Energies, MDPI, vol. 11(3), pages 1-35, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:288:y:2021:i:c:s0306261921001252. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.