Probabilistic risk analysis and building performance simulations : Building design optimisation and quantifying stakeholder consequences
(2021) In Energy and Buildings 252.- Abstract
- A method for risk analysis and building performance simulations is developed to optimise the building design process and fulfil the design criteria. The aim is to support the decision-makers during the building design process by including uncertainties from the design phase, quantifying the probability of attaining the energy performance criteria and financial requirements. The method is meant to be used as a comparative study of optional building designs based on different stakeholder values and consequence models to quantify the economic consequences of failing to comply with energy requirements. A case study using
single-family houses demonstrated the various applications of the method using two design options as examples for... (More) - A method for risk analysis and building performance simulations is developed to optimise the building design process and fulfil the design criteria. The aim is to support the decision-makers during the building design process by including uncertainties from the design phase, quantifying the probability of attaining the energy performance criteria and financial requirements. The method is meant to be used as a comparative study of optional building designs based on different stakeholder values and consequence models to quantify the economic consequences of failing to comply with energy requirements. A case study using
single-family houses demonstrated the various applications of the method using two design options as examples for quantifying the probability of failure and the probability of a design option being more financially viable than the other. The energy performance was simulated based on fifteen stochastic parameters, and the cost evaluation was simulated using six stochastic parameters from three stakeholders with different values and two different consequence models. The results showed a difference between stakeholders in the case study; the cheaper option was more suitable for the property developer while the more expensive option was a better choice for the private building owner if a longer lifecycle was expected. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/77d5e51f-68e9-4233-b60a-54bbf7766777
- author
- Ekström, Tomas LU ; Sundling, Rikard LU ; Burke, Stephen LU and Harderup, Lars Erik LU
- organization
- publishing date
- 2021-12-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Dynamic energy simulations, LCC, Monte Carlo method, Probabilistic energy simulations, Probabilistic risk analysis, Uncertainty analysis
- in
- Energy and Buildings
- volume
- 252
- article number
- 111434
- publisher
- Elsevier
- external identifiers
-
- scopus:85115420627
- ISSN
- 0378-7788
- DOI
- 10.1016/j.enbuild.2021.111434
- project
- Predicting the Energy Performance of Buildings - A Method using Probabilistic Risk Analysis for Data-driven Decision-support
- language
- English
- LU publication?
- yes
- id
- 77d5e51f-68e9-4233-b60a-54bbf7766777
- date added to LUP
- 2021-08-18 14:38:30
- date last changed
- 2023-04-02 08:45:28
@article{77d5e51f-68e9-4233-b60a-54bbf7766777,
abstract = {{A method for risk analysis and building performance simulations is developed to optimise the building design process and fulfil the design criteria. The aim is to support the decision-makers during the building design process by including uncertainties from the design phase, quantifying the probability of attaining the energy performance criteria and financial requirements. The method is meant to be used as a comparative study of optional building designs based on different stakeholder values and consequence models to quantify the economic consequences of failing to comply with energy requirements. A case study using<br/>single-family houses demonstrated the various applications of the method using two design options as examples for quantifying the probability of failure and the probability of a design option being more financially viable than the other. The energy performance was simulated based on fifteen stochastic parameters, and the cost evaluation was simulated using six stochastic parameters from three stakeholders with different values and two different consequence models. The results showed a difference between stakeholders in the case study; the cheaper option was more suitable for the property developer while the more expensive option was a better choice for the private building owner if a longer lifecycle was expected.}},
author = {{Ekström, Tomas and Sundling, Rikard and Burke, Stephen and Harderup, Lars Erik}},
issn = {{0378-7788}},
keywords = {{Dynamic energy simulations; LCC; Monte Carlo method; Probabilistic energy simulations; Probabilistic risk analysis; Uncertainty analysis}},
language = {{eng}},
month = {{12}},
publisher = {{Elsevier}},
series = {{Energy and Buildings}},
title = {{Probabilistic risk analysis and building performance simulations : Building design optimisation and quantifying stakeholder consequences}},
url = {{http://dx.doi.org/10.1016/j.enbuild.2021.111434}},
doi = {{10.1016/j.enbuild.2021.111434}},
volume = {{252}},
year = {{2021}},
}