Journal
ENERGY
Volume 152, Issue -, Pages 818-833Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2018.03.168
Keywords
Bayesian calibration; Model selection and validation; Dynamic thermal models; Real house experiment; Improved metropolis-Hastings algorithm; Robust gradient and Hessian computation
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Funding
- BPI France in the FUI Project COMETE
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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. (C) 2018 Elsevier Ltd. All rights reserved.
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