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An efficient Bayesian experimental calibration of dynamic thermal models

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  • Raillon, L.
  • Ghiaus, C.

Abstract

Experimental calibration of dynamic thermal models is required for model predictive control and characterization of building energy performance. In these applications, the uncertainty assessment of the parameter estimates is decisive; this is why a Bayesian calibration procedure (selection, calibration and validation) is presented. The calibration is based on an improved Metropolis-Hastings algorithm suitable for linear and Gaussian state-space models. The procedure, illustrated on a real house experiment, shows that the algorithm is more robust to initial conditions than a maximum likelihood optimization with a quasi-Newton algorithm. Furthermore, when the data are not informative enough, the use of prior distributions helps to regularize the problem.

Suggested Citation

  • Raillon, L. & Ghiaus, C., 2018. "An efficient Bayesian experimental calibration of dynamic thermal models," Energy, Elsevier, vol. 152(C), pages 818-833.
    • Handle: RePEc:eee:energy:v:152:y:2018:i:c:p:818-833
    DOI: 10.1016/j.energy.2018.03.168
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    References listed on IDEAS

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    1. Kulikova, M.V. & Tsyganova, J.V., 2016. "A unified square-root approach for the score and Fisher information matrix computation in linear dynamic systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 119(C), pages 128-141.
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    Cited by:

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    2. 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).
    3. Manfren, Massimiliano & Nastasi, Benedetto & Tronchin, Lamberto & Groppi, Daniele & Garcia, Davide Astiaso, 2021. "Techno-economic analysis and energy modelling as a key enablers for smart energy services and technologies in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    4. Cao, Bo & Cui, Weijie & Chen, Chao & Chen, Yixue, 2020. "Development and uncertainty analysis of radionuclide atmospheric dispersion modeling codes based on Gaussian plume model," Energy, Elsevier, vol. 194(C).
    5. Lukas Lundström & Jan Akander, 2019. "Bayesian Calibration with Augmented Stochastic State-Space Models of District-Heated Multifamily Buildings," Energies, MDPI, vol. 13(1), pages 1-28, December.
    6. Tohme, Tony & Vanslette, Kevin & Youcef-Toumi, Kamal, 2020. "A generalized Bayesian approach to model calibration," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    7. Hou, D. & Hassan, I.G. & Wang, L., 2021. "Review on building energy model calibration by Bayesian inference," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    8. Na, Wei & Wang, Mingming, 2022. "A Bayesian approach with urban-scale energy model to calibrate building energy consumption for space heating: A case study of application in Beijing," Energy, Elsevier, vol. 247(C).

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