Uncertainty of the predictions of different programs and modelling teams based on a detailed empirical validation dataset

Abstract

The results obtained from programs predicting the thermal-energetic performance of buildings depend on the assumptions made when parameterizing the constructed models. This is because some parameters are not known or are subject to uncertainty. Common simulation programs are required to undergo rigorous quality control and validation. These involve analysis of predictions against analytical test cases, inter-model comparisons such as BESTEST (ASHRAE. 2020. ASHRAE 140-2020 Standard Method of Test for the Evaluation of Building Energy Analysis Computer Programs) and empirical validations against measurement data. This paper uses an empirical validation data set and corresponding modelling predictions from IEA EBC Annex 71 to analyse the uncertainty of different programs by means of a one-step-at-a-time sensitivity analysis. Since the results show a strong influence from the various sub-model calculations of the air flows, an additional sensitivity analysis is added regarding the air exchange through the envelope and between the zones .

KEYWORDS:

• Building energy simulation
• empirical validation
• measurement dataset
• sensitivity analysis
• airflow inside buildings

Acknowledgements

The contributions of the Fraunhofer Institute for Building Physics IBP were funded by the German Federal Ministry of Energy and Economic Affairs BMWi (now: Federal Ministry of Economic Affairs and Climat Action BMWK) under the grant number 03ET1509A. The contributions of the University of Innsbruck were carried out and funded as part of the IEA research cooperation on behalf of the Austrian Federal Ministry for Climate Protection, Environment, Energy, Mobility, Innovation and Technology (BMK). The sizing simulation of the experiment and sensitivity analysis for the experimental design (Mantesi, et al., 2019) (not the result’s analysis discussed in this paper) was undertaken by Eirini Mantesi, Kostas Mourkos, Christina Hopfe, Robert McLeod and Paraskevi Vatougiou from the School of Architecture, Building & Civil Engineering, Loughborough University (Mantesi, et al., 2019). The thermal bridge analyses were undertaken by Jørgen Rose from the Danish Building Research Institute, SBi, Aalborg University using HEAT2, and Felix Thomas from the Leeds Sustainability Institute, School of Built Environment and Engineering, Leeds Beckett University using TRISCO. The stochastic user profiles for the experiment and the sensitivity analysis were provided by Graeme Flett of the Energy Systems Research Unit, University of Strathclyde, Glasgow, G1 1XJ, UK based on (Flett and Kelly 2016) and (Flett and Kelly 2017). Max Blöchle from Visplore GmbH supported data pre-processing for visualization of airflow modelling results with the software visplore.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are openly available in FORDATIS at https://fordatis.fraunhofer.de/handle/fordatis/161.2.

Additional information

Funding

This work was supported by the Austrian Federal Ministry for Climate Protection, Environment, Energy, Mobility, Innovation and Technology [BMK, grant number FFG 864144]; Federal Ministry of Economic Affairs and Climat Action BMWK [grant number 03ET1509A].