Evaluating the impact of data quality on the accuracy of the predicted energy performance for a fixed building design using probabilistic energy performance simulations and uncertainty analysis
(2021) In Energy and Buildings 249.- Abstract
- Probabilistic building performance simulations enables a method for evaluating how the data quality used to create input models affects the accuracy of the predicted energy performance. The method described in this paper explores an uncertainty analysis of a fixed building design and system by including the discrepancy between the declared property of a product, material, or behaviour quantified under specific conditions, and the actual performance in real-world use. The method was tested using a case study, a multi-family building, using two datasets based on different data quality to quantify the discrepancies. The models' outcome was validated against field measurements from 28 buildings built using the fixed building design. The main... (More)
- Probabilistic building performance simulations enables a method for evaluating how the data quality used to create input models affects the accuracy of the predicted energy performance. The method described in this paper explores an uncertainty analysis of a fixed building design and system by including the discrepancy between the declared property of a product, material, or behaviour quantified under specific conditions, and the actual performance in real-world use. The method was tested using a case study, a multi-family building, using two datasets based on different data quality to quantify the discrepancies. The models' outcome was validated against field measurements from 28 buildings built using the fixed building design. The main findings were that data quality significantly shifted the probability density curves and consequently impacted the predictions and accuracy of the predictive models, showing the importance of high-quality input models and a validation process based on a probability distribution. The study also indicated that higher quality data does not equal narrower distributions in the input models. In this case, the case study showed that, despite well-known building properties, narrowing the performance gap can only occur through larger variation in the input data models, resulting in a larger predicted energy performance interval. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/6511da6a-e083-4c15-9aef-9004279d6caa
- author
- Ekström, Tomas LU ; Burke, Stephen LU ; Wiktorsson, Magnus LU ; Hassanie, Samer ; Harderup, Lars-Erik LU and Arfvidsson, Jesper LU
- organization
- publishing date
- 2021-10-15
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Energy and Buildings
- volume
- 249
- article number
- 111205
- publisher
- Elsevier
- external identifiers
-
- scopus:85109465437
- ISSN
- 0378-7788
- DOI
- 10.1016/j.enbuild.2021.111205
- project
- Predicting the Energy Performance of Buildings - A Method using Probabilistic Risk Analysis for Data-driven Decision-support
- language
- English
- LU publication?
- yes
- id
- 6511da6a-e083-4c15-9aef-9004279d6caa
- date added to LUP
- 2021-06-28 21:30:12
- date last changed
- 2024-01-05 11:44:17
@article{6511da6a-e083-4c15-9aef-9004279d6caa,
abstract = {{Probabilistic building performance simulations enables a method for evaluating how the data quality used to create input models affects the accuracy of the predicted energy performance. The method described in this paper explores an uncertainty analysis of a fixed building design and system by including the discrepancy between the declared property of a product, material, or behaviour quantified under specific conditions, and the actual performance in real-world use. The method was tested using a case study, a multi-family building, using two datasets based on different data quality to quantify the discrepancies. The models' outcome was validated against field measurements from 28 buildings built using the fixed building design. The main findings were that data quality significantly shifted the probability density curves and consequently impacted the predictions and accuracy of the predictive models, showing the importance of high-quality input models and a validation process based on a probability distribution. The study also indicated that higher quality data does not equal narrower distributions in the input models. In this case, the case study showed that, despite well-known building properties, narrowing the performance gap can only occur through larger variation in the input data models, resulting in a larger predicted energy performance interval.}},
author = {{Ekström, Tomas and Burke, Stephen and Wiktorsson, Magnus and Hassanie, Samer and Harderup, Lars-Erik and Arfvidsson, Jesper}},
issn = {{0378-7788}},
language = {{eng}},
month = {{10}},
publisher = {{Elsevier}},
series = {{Energy and Buildings}},
title = {{Evaluating the impact of data quality on the accuracy of the predicted energy performance for a fixed building design using probabilistic energy performance simulations and uncertainty analysis}},
url = {{http://dx.doi.org/10.1016/j.enbuild.2021.111205}},
doi = {{10.1016/j.enbuild.2021.111205}},
volume = {{249}},
year = {{2021}},
}