An efficient Bayesian experimental calibration of dynamic thermal models

L. Raillon and C. Ghiaus

Energy, 2018, vol. 152, issue C, 818-833

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.

Keywords: Bayesian calibration; Model selection and validation; Dynamic thermal models; Real house experiment; Improved metropolis-Hastings algorithm; Robust gradient and Hessian computation (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:152:y:2018:i:c:p:818-833

DOI: 10.1016/j.energy.2018.03.168

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