Egan, James and Finn, Donal and Deogene Soares, Pedro Henrique and Rocha Baumann, Victor Andreas and Aghamolaei, Reihaneh and Beagon, Paul and Neu, Olivier and Pallonetto, Fabiano and O’Donnell, James (2018) Definition of a useful minimal-set of accurately-specified input data for Building Energy Performance Simulation. Energy and Buildings, 165. pp. 172-183. ISSN 03787788

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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.

Item Type:	Article
Keywords:	Building simulation; Sensitivity analysis; Influence coefficient; Simulation accuracy; Input data;
Academic Unit:	Faculty of Science and Engineering > Research Institutes > Hamilton Institute Faculty of Social Sciences > Research Institutes > Innovation Value Institute, IVI Faculty of Social Sciences > School of Business
Item ID:	15605
Identification Number:	https://doi.org/10.1016/j.enbuild.2018.01.012
Depositing User:	Fabiano Pallonetto
Date Deposited:	01 Mar 2022 16:32
Journal or Publication Title:	Energy and Buildings
Publisher:	Elsevier
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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