Possibilities with Probabilistic Methods for Dynamic Building Energy Simulations using Stochastic Input Data : – Initial Analysis
(2019) International Conference on Thermal Performance of the Exterior Envelopes of Whole Buildings XIV p.840-840- Abstract
- As observed in earlier studies, there is evidently a performance gap between the predicted annual energy use from building performance simulations based on traditional deterministic methods compared to the monitored annual energy use of a building. The hypothesis is that using a probabilistic method makes it possible to consider the uncertainties in the input data and quantify the overall uncertainty of a building design using a probability distribution for the predicted energy performance of a building. Thus, reducing the performance gap between the predicted and monitored energy use. This paper aims to detail the advantages and disadvantages of both the deterministic and the probabilistic methods when determining the energy performance... (More)
- As observed in earlier studies, there is evidently a performance gap between the predicted annual energy use from building performance simulations based on traditional deterministic methods compared to the monitored annual energy use of a building. The hypothesis is that using a probabilistic method makes it possible to consider the uncertainties in the input data and quantify the overall uncertainty of a building design using a probability distribution for the predicted energy performance of a building. Thus, reducing the performance gap between the predicted and monitored energy use. This paper aims to detail the advantages and disadvantages of both the deterministic and the probabilistic methods when determining the energy performance of a building and evaluate the differences based on a qualitative analysis. The differences between the methods are evaluated further based on the results from a previous case study where the probabilistic method has been implemented in two dynamic building performance simulation software. The conclusion from this study is that both methods have their specific advantages and disadvantages, however the main differentiating point is the scope of application. The deterministic method is a simpler alternative, needing a less amount of data and is performed in less time, thus making it advantageous in early phases when the basic design of a building is decided, and available information still is limited. However, this method must make use of an arbitrary margin of safety when used for code compliance. The perceived accuracy of the results, since the software reports the result to several decimals, are often misleading since the numerical value says nothing about the probability of fulfilling the requirements. The probabilistic method is more robust and requires more information, such as a larger quantity of data for each factor, and more advanced knowledge of both energy performance and statistics from the operator. Because of this, it also requires more computational power and is more time consuming. Thus, the method is more advantageous for analysis and determining the risks associated with not fulfilling the building regulations, since the method determines the probability of failure, instead of using an arbitrary margin of safety. (Less)
- Abstract (Swedish)
- As observed in earlier studies, there is evidently a performance gap between the predicted annual energy use from building performance simulations based
on traditional deterministic methods compared to the monitored annual energy use of a building. The hypothesis is that using a probabilistic method makes it possible to consider the uncertainties in the input data and quantify the overall uncertainty of a building design using a probability distribution for the predicted energy performance of a building. Thus, reducing the performance gap between the predicted and monitored energy use. This paper aims to detail the advantages and disadvantages of both the deterministic and the probabilistic methods when determining the energy... (More) - As observed in earlier studies, there is evidently a performance gap between the predicted annual energy use from building performance simulations based
on traditional deterministic methods compared to the monitored annual energy use of a building. The hypothesis is that using a probabilistic method makes it possible to consider the uncertainties in the input data and quantify the overall uncertainty of a building design using a probability distribution for the predicted energy performance of a building. Thus, reducing the performance gap between the predicted and monitored energy use. This paper aims to detail the advantages and disadvantages of both the deterministic and the probabilistic methods when determining the energy performance of a building and
evaluate the differences based on a qualitative analysis. The differences between the methods are evaluated further based on the results from a previous case
study where the probabilistic method has been implemented in two dynamic building performance simulation software. The conclusion from this study is
that both methods have their specific advantages and disadvantages, however the main differentiating point is the scope of application. The deterministic
method is a simpler alternative, needing a less amount of data and is performed in less time, thus making it advantageous in early phases when the basic
design of a building is decided, and available information still is limited. However, this method must make use of an arbitrary margin of safety when used
for code compliance. The perceived accuracy of the results, since the software reports the result to several decimals, are often misleading since the numerical
value says nothing about the probability of fulfilling the requirements. The probabilistic method is more robust and requires more information, such as a
larger quantity of data for each factor, and more advanced knowledge of both energy performance and statistics from the operator. Because of this, it also
requires more computational power and is more time consuming. Thus, the method is more advantageous for analysis and determining the risks
associated with not fulfilling the building regulations, since the method determines the probability of failure, instead of using an arbitrary margin of safety. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/d01dc8e6-84a9-4712-9328-8012fddc3613
- author
- Ekström, Tomas LU ; Harderup, Lars-Erik LU ; Arfvidsson, Jesper LU and Burke, Stephen LU
- organization
- publishing date
- 2019
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Proceedings of the Thermal Performance of the Exterior Envelopes of Whole Buildings XIV
- pages
- 849 pages
- conference name
- International Conference on Thermal Performance of the Exterior Envelopes of Whole Buildings XIV
- conference location
- Clearwater, United States
- conference dates
- 2019-12-09 - 2019-12-12
- external identifiers
-
- scopus:85103071803
- ISBN
- 978-1-947192-44-7
- project
- Predicting the Energy Performance of Buildings - A Method using Probabilistic Risk Analysis for Data-driven Decision-support
- language
- English
- LU publication?
- yes
- id
- d01dc8e6-84a9-4712-9328-8012fddc3613
- date added to LUP
- 2020-01-27 13:56:23
- date last changed
- 2022-04-18 20:26:16
@inproceedings{d01dc8e6-84a9-4712-9328-8012fddc3613,
abstract = {{As observed in earlier studies, there is evidently a performance gap between the predicted annual energy use from building performance simulations based on traditional deterministic methods compared to the monitored annual energy use of a building. The hypothesis is that using a probabilistic method makes it possible to consider the uncertainties in the input data and quantify the overall uncertainty of a building design using a probability distribution for the predicted energy performance of a building. Thus, reducing the performance gap between the predicted and monitored energy use. This paper aims to detail the advantages and disadvantages of both the deterministic and the probabilistic methods when determining the energy performance of a building and evaluate the differences based on a qualitative analysis. The differences between the methods are evaluated further based on the results from a previous case study where the probabilistic method has been implemented in two dynamic building performance simulation software. The conclusion from this study is that both methods have their specific advantages and disadvantages, however the main differentiating point is the scope of application. The deterministic method is a simpler alternative, needing a less amount of data and is performed in less time, thus making it advantageous in early phases when the basic design of a building is decided, and available information still is limited. However, this method must make use of an arbitrary margin of safety when used for code compliance. The perceived accuracy of the results, since the software reports the result to several decimals, are often misleading since the numerical value says nothing about the probability of fulfilling the requirements. The probabilistic method is more robust and requires more information, such as a larger quantity of data for each factor, and more advanced knowledge of both energy performance and statistics from the operator. Because of this, it also requires more computational power and is more time consuming. Thus, the method is more advantageous for analysis and determining the risks associated with not fulfilling the building regulations, since the method determines the probability of failure, instead of using an arbitrary margin of safety.}},
author = {{Ekström, Tomas and Harderup, Lars-Erik and Arfvidsson, Jesper and Burke, Stephen}},
booktitle = {{Proceedings of the Thermal Performance of the Exterior Envelopes of Whole Buildings XIV}},
isbn = {{978-1-947192-44-7}},
language = {{eng}},
pages = {{840--840}},
title = {{Possibilities with Probabilistic Methods for Dynamic Building Energy Simulations using Stochastic Input Data : – Initial Analysis}},
year = {{2019}},
}