Calibration represents a crucial step in the modelling process to obtain accurate simulation results and quantify uncertainties. We scrutinize the statistical Kennedy & O’Hagan framework, which quantifies different sources of uncertainty in the calibration process, including both model inputs and errors in the model. In specific, we evaluate the influence of error terms on the posterior predictions of calibrated model inputs. We do so by using a simulation model of a heat pump in cooling mode. While posterior values of many parameters concur with the expectations, some parameters appear not to be inferable. This is particularly true for parameters associated with model discrepancy, for which prior knowledge is typically scarce. We reveal the importance of assessing the identifiability of parameters by exploring the dependency of posteriors on the assigned prior knowledge. Analyses with random datasets show that results are overall consistent, which confirms the applicability and reliability of the framework.
Bibliographical noteFunding Information:
This study was conducted as part of the ‘Bayesian Building Energy Management (B.bem)’ project funded by the Engineering and Physical Sciences Research Council of the United Kingdom (EPSRC reference: EP/L024454/1).
© 2018, © 2018 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
- Bayesian inference
- building energy model
- energy system model
- inverse problems
- model calibration
- uncertainty quantification
ASJC Scopus subject areas
- Building and Construction
- Modelling and Simulation
- Computer Science Applications