Bayesian calibration of building energy models for uncertainty analysis through test cells monitoring

Carmen María Calama-González, Phil Symonds, Giorgos Petrou, Rafael Suárez and Ángel Luis León-Rodríguez

Applied Energy, 2021, vol. 282, issue PA, No S0306261920315361

Abstract: Improving the energy efficiency of existing buildings is a priority for meeting energy consumption and CO2 emission targets in buildings. Building simulation tools play a crucial role in evaluating the performance of energy retrofit options. In this paper, a Bayesian calibration approach is applied to reduce the discrepancies between measured and simulated temperature data. Through its application to a test cell case study, the incorporation of sensitivity analysis and Bayesian calibration techniques are proven to improve the level of agreement between on-site measurements and simulated outputs, whilst accounting for both experimental and simulation uncertainties. The accuracy of a building simulation model developed using EnergyPlus was evaluated before and after calibration. Uncalibrated models were within the uncertainty ranges specified by the ASHARE Guidelines, with hourly simulation data over-predicting measurements by 3.2 °C on average. After Bayesian calibration, the average maximum temperature difference was reduced to around 0.68 °C, an improvement of almost 80%.

Keywords: Bayesian calibration; Sensitivity analysis; Uncertainty analysis; Building energy modelling; Mediterranean climate; Housing stock (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (6)

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DOI: 10.1016/j.apenergy.2020.116118

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