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Definition of a useful minimal-set of accurately-specified input data for Building Energy Performance Simulation
Date Issued
2018-04-15
Date Available
2019-08-20T10:04:30Z
Abstract
Developing BEPS models which predict energy usage to a high degree of accuracy can be extremely time consuming. As a result, assumptions are often made regarding the input data required. Making these assumptions without introducing a significant amount of uncertainty to the model can be difficult, and requires experience. Even so, rules of thumb from one geographic region are not automatically transferrable to other regions. This paper develops a methodology which can be used to determine useful guidelines for defining the most influential input data for an accurate BEPS model. Differential sensitivity analysis is carried out on parametric data gathered from five archetype dwelling models. The sensitivity analysis results are used in order to form a guideline minimum set of accurately defined input data. Although the guidelines formed apply specifically to Irish residential dwellings, the methodology and processes used in defining the guidelines is highly repeatable. The guideline minimum data set was applied to practical examples in order to be validated. Existing buildings were modelled, and only the parameters within the minimum data set are accurately defined. All building models predict annual energy usage to within 10% of actual measured data, with seasonal energy profiles well-matching.
Sponsorship
European Commission - Seventh Framework Programme (FP7)
Science Foundation Ireland
Other Sponsorship
Science Program Without Borders/CAPES - Brazil
Type of Material
Journal Article
Publisher
Elsevier BV
Journal
Energy and Buildings
Volume
165
Start Page
172
End Page
183
Copyright (Published Version)
2018 Elsevier
Language
English
Status of Item
Peer reviewed
ISSN
0378-7788
This item is made available under a Creative Commons License
File(s)
No Thumbnail Available
Name
2018_JamesEganSensitivity.pdf
Size
2.72 MB
Format
Adobe PDF
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cab9484a3971795cdef852711b179c02
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