Issue |
E3S Web Conf.
Volume 172, 2020
12th Nordic Symposium on Building Physics (NSB 2020)
|
|
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Article Number | 25005 | |
Number of page(s) | 8 | |
Section | Energy performance simulation and assessment | |
DOI | https://doi.org/10.1051/e3sconf/202017225005 | |
Published online | 30 June 2020 |
Proposed method for probabilistic risk analysis using building performance simulations and stochastic parameters
1 Division of Building Physics at the department of Building and Environmental Technology, Lund University
2 NCC AB
* Corresponding author: tomas.ekstrom@ncc.se
As parts of the world continue the work of mitigating the impact of climate change, many countries strive for continued reductions in energy demand from buildings by implementing more stringent building regulations. Consequently, the importance of accurate and efficient building performance simulations to predict the energy use of a building design increases. As observed in earlier studies, there are performance gaps between the predicted annual energy demand from building energy performance simulations based on deterministic methods compared to the monitored annual energy use of a building. This paper presents a preliminary method developed using probabilistic methods for risk analysis and building performance simulations to predict the energy performance of buildings using stochastic parameters. The method is used to calculate the probability for the energy performance of a building design to fulfil the energy requirements. The consequences are quantified using an example of energy performance contracting to evaluate the inherent risk of a building’s design. The method was demonstrated in a case study and validated by comparing the results in energy performance and probability of failure against measured data from 26 single-family houses.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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