Development of occupancy-integrated archetypes: Use of data mining clustering techniques to embed occupant behaviour profiles in archetypes

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Title: Development of occupancy-integrated archetypes: Use of data mining clustering techniques to embed occupant behaviour profiles in archetypes
Authors: Buttitta, GiuseppinaTurner, William J. N.Neu, OlivierFinn, Donal
Permanent link: http://hdl.handle.net/10197/11565
Date: 1-Sep-2019
Online since: 2020-09-15T13:22:00Z
Abstract: Building stock modelling usually deploys representative building archetypes to obtain reliable results of annual energy heating demand and to minimise the associated computational cost. Available methodologies define archetypes considering only the physical characteristics of buildings. Uniform occupancy schedules, which correspond to national averages, are generally used in archetype energy simulations, despite evidence of occupancy schedules which can vary considerably for each building. This paper presents a new methodology to define occupancy-integrated archetypes. The novel feature of these archetype models is the integration of different occupancy schedules within the archetype itself. This allows building stock energy simulations of national population subgroups characterised by specific occupancy profiles to be undertaken. The importance of including occupant-related data in residential archetypes, which is different than the national average, is demonstrated by applying the methodology to the UK national building stock. The resultant occupancy-integrated archetypes are then modelled to obtain the annual final heating energy demand. It is shown that the relative difference between the heating demand of occupancy-integrated archetypes and uniform occupancy archetypes can be up to 30%.
Funding Details: European Commission Horizon 2020
Type of material: Journal Article
Publisher: Elsevier
Journal: Energy and Buildings
Volume: 198
Start page: 84
End page: 99
Copyright (published version): 2019 Elsevier
Keywords: Residential buildingsArchetypesStock modellingk-mode clusteringOccupancy profilesEnergy consumptionBuilding stockResidential buildingsDomestic occupancy
DOI: 10.1016/j.enbuild.2019.05.056
Language: en
Status of Item: Peer reviewed
Appears in Collections:Mechanical & Materials Engineering Research Collection

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