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Calama-González, C. M., Symonds, P., Petrou, G., Suárez, R., & León-Rodríguez, Á. L. (2021). Bayesian calibration of building energy models for uncertainty analysis through test cells monitoring.

REVISTA: Applied Energy, 282(116118), 1-16.

IMPACTO:  JCR (2019): 8,848. ENERGY & FUELS, (Q1) (9/112)

DOI: https://doi.org/10.1016/j.apenergy.2020.116118

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 by3.2 ºC on average. After Bayesian calibration, the average maximum temperature difference was reduced to around 0.68 ºC, an improvement of almost 80%.

Highlights:

Calibrating energy simulation models is crucial when assessing existing buildings.
Sensitivity analysis is key to reduce computational time in the calibration process.
Uncertainty techniques may be applied to assess energy models’ accuracy.
Test Cells allow the performance of building simulation tools to be estimated.