Abstract
Abstract
Resource scarcity and anthropogenic climate change require the reduction of performance gaps in existing buildings. In addition to unexpected user behavior, performance gaps are primarily caused by the technical gap due to operational errors in building technology. The main objective of this paper is to quantify model input uncertainty incorporating uncertain boundary conditions in terms of operational errors using thermo-dynamic building performance simulations and to identify the most relevant input parameters for the performance gaps in air conditioning systems by means of sensitivity analyses. Model input uncertainty is stochastically determined using Monte-Carlo Simulations to calculate the target values “primary energy demand” as well as “over- and under-temperature degree hours” for an office building. Selected parameters are simulated in a specific uncertainty and sensitivity analyses using the Sobol’ and Jansen estimators, which distinguish between a direct influence on the target variables and interactions between the parameters. The methodology requires a selection process, which is carried out as part of relative uncertainty and relative sensitivity analyses. Furthermore, the operational errors are compared with construction factors as well as building physics inputs and design parameters for building technology systems to show their reciprocal effects as part of a comprehensive investigation. The main findings of this paper are that operational errors in air conditioning systems play an essential role in decreasing energy efficiency and thermal comfort, but do not warrant the significance of certain construction factors as well as setpoints in building technology. Moreover, the impact of operational errors on thermal overheating of the building investigated is minor compared to other targets that cause greater model input uncertainty.
Funder
Technische Universität München
Publisher
Springer Science and Business Media LLC
Reference39 articles.
1. Auer, T., Lauss, L., et al. (2020). Big Data in der Gebäudeautomation - Big Data Analysen von Automationsdaten zur energetischen Betriebsoptimierung des Gebäudebestandes, Research Report, Chair of Building Technology and Climate Responsive Design, Technical University Munich. https://doi.org/10.14459/2020md1546757. Can be downloaded at: https://mediatum.ub.tum.de/doc/1546757/1546757.pdf
2. Brohus, H. & Heiselberg, P. (2009). Uncertainty of energy consumption assessment of domestic buildings. Glasgow. Can be downloaded at: http://www.ibpsa.org/procee-dings/BS2009/BS09_1022_1029.pdf
3. Burhenne, S. (2013). Monte Carlo based uncertainty and sensitivity analysis for building performance simulation. p. 208, Can be ordered from: https://www.shaker.de, ISBN: 978–3–8440–2502–6
4. Burhenne, S., Jacob, D. & Henze, G. (2011). Sampling based on Sobol’ sequences for Monte Carlo techniques applied to building simulations. p. 1821, Can be downloaded at: https://www.researchgate.net/publication/257139589_Sampling_based_on_Sobol%27_sequences_for_Monte_Carlo_techniques_applied_to_building_simulations
5. Campolongo, F., Saltelli, A., and Cariboni, J. (2011). From screening to quantitative sensitivity analysis. A unified approach. Computer Physics Communications, 182(4):978–988, Can be ordered from: https://www.sciencedirect.com
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