An efficient Bayesian experimental calibration of dynamic thermal models

作者：Raillon L^*; Ghiaus C

来源：Energy, 2018, 152: 818-833.

DOI：10.1016/j.energy.2018.03.168

• Bayesian calibration
• Model selection and validation
• Dynamic thermal models
• Real house experiment
• Improved metropolis-Hastings algorithm
• Robust gradient and Hessian computation

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.

• 出版日期2018-6-1