Egan, JamesJamesEganFinn, DonalDonalFinnDeogene Soares, Pedro HenriquePedro HenriqueDeogene SoaresRocha Baumann, Victor AndreasVictor AndreasRocha BaumannAghamolaei, ReihanehReihanehAghamolaeiBeagon, PaulPaulBeagonNeu, OlivierOlivierNeuPallonetto, FabianoFabianoPallonettoO'Donnell, JamesJamesO'Donnell2019-08-202019-08-202018 Elsev2018-04-15Energy and Buildings0378-7788http://hdl.handle.net/10197/11002Developing 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.enThis is the author’s version of a work that was accepted for publication in Energy & Buildings. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Energy and Buildings (165, (2018)) https://doi.org/10.1016/j.enbuild.2018.01.012Building simulationSensitivity analysisInfluence coefficientSimulation accuracyInput dataJournal Article16517218310.1016/j.enbuild.2018.01.0122019-08-1515/SPP/E3125Project number 631617https://creativecommons.org/licenses/by-nc-nd/3.0/ie/