Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Bayesian calibration of building energy models for uncertainty analysis through test cells monitoring
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%.
Bayesian calibration of building energy models for uncertainty analysis through test cells monitoring
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%.
Bayesian calibration of building energy models for uncertainty analysis through test cells monitoring
Calama-González, CM (Autor:in) / Symonds, P (Autor:in) / Petrou, G (Autor:in) / Suárez, R (Autor:in) / León-Rodríguez, ÁL (Autor:in)
15.01.2021
Applied Energy , 282 (A) , Article 116118. (2021)
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
DDC:
690
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