2021
DOI: 10.1016/j.rser.2021.110930
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Review on building energy model calibration by Bayesian inference

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Cited by 65 publications
(18 citation statements)
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“…Urban energy modelling is no exception, and this step is required to improve the precision of the results and bring the virtual energy model closer to reality. For urban energy modelling calibration, Bayesian inference is "the process of fitting a probability model to a set of data and summarizing the results by a probability distribution on parameters of the model and unobserved quantities such as predictions of new observations" [50]. It is gaining interest and has been studied by different researchers in recent years.…”
Section: Urban Building Energy Modellingmentioning
confidence: 99%
“…Urban energy modelling is no exception, and this step is required to improve the precision of the results and bring the virtual energy model closer to reality. For urban energy modelling calibration, Bayesian inference is "the process of fitting a probability model to a set of data and summarizing the results by a probability distribution on parameters of the model and unobserved quantities such as predictions of new observations" [50]. It is gaining interest and has been studied by different researchers in recent years.…”
Section: Urban Building Energy Modellingmentioning
confidence: 99%
“…This uncertainty is often referred to as an "energy performance gap" in literature (Cozza et al, 2021). Effective management of the validation process is crucial for establishing a credible BES model (Hou et al, 2021). However, Malhotra et al reported that among 72 reviewed articles, only 7% conducted a validation study, indicating a limited practice (Malhotra, 2022).…”
Section: Sasbementioning
confidence: 99%
“…Under the ideal situation, all the uncertain parameters in the BEM calculation model should be calibrated [20]. However, due to the data quantity/quality limitation, it is a common practice to identify the uncertain parameters using the SA technique [30].…”
Section: Step 1: Sensitive Analysismentioning
confidence: 99%
“…It has been applied for different building functions (i.e., residential buildings [16], commercial buildings [17]) and different scales of buildings (individual buildings, building clusters [18] and building stocks [19]). Additional focus was put on the statistic performance of different emulators or surrogates under a BC framework [20]. Lim and Zhai [21] compared five meta-models for BC and indicated that the Gaussian-process (GP) model is relatively accurate.…”
Section: Introductionmentioning
confidence: 99%