Journal
JOURNAL OF BUILDING PERFORMANCE SIMULATION
Volume 12, Issue 1, Pages 82-96Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/19401493.2018.1475506
Keywords
Bayesian inference; model calibration; building energy model; energy system model; uncertainty quantification; inverse problems
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Funding
- Engineering and Physical Sciences Research Council of the United Kingdom [EP/L024454/1]
- EPSRC [EP/I019308/1, EP/L010917/1, EP/L024454/1, EP/K000314/1] Funding Source: UKRI
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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.
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