An efficient Bayesian experimental calibration of dynamic thermal models

TL;DR

This paper presents an efficient Bayesian calibration method for dynamic thermal models, improving robustness and regularization in parameter estimation for building energy applications.

Contribution

It introduces an improved Metropolis-Hastings algorithm tailored for linear Gaussian models, enhancing calibration robustness over traditional optimization methods.

Findings

The Bayesian method outperforms maximum likelihood in robustness to initial conditions.

Prior distributions effectively regularize when data are insufficient.

The approach is validated on real building data.

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.