Modelling Household Occupancy Profiles using Data Mining Clustering Techniques on Time Use Data

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Title: Modelling Household Occupancy Profiles using Data Mining Clustering Techniques on Time Use Data
Authors: Buttitta, Giuseppina
Neu, Olivier
Turner, William J. N.
Finn, Donal
Permanent link: http://hdl.handle.net/10197/9218
Date: 9-Aug-2017
Abstract: A strong correlation exists between occupant behaviour and energy demand in residential buildings. The choice of the most suitable occupancy model to be integrated in high temporal resolution energy demand simulations is heavily in uenced by the purpose of the building energy demand model and it is a tradeoff between complexity and accuracy. The current paper introduces a new occupancy model that produces multi-day occupancy profiles and can be adaptable to various occupancy scenarios (e.g., at home all day, mostly absent) and scalable to different population sizes. The methodology exploits data mining clustering techniques with Time Use Survey (TUS) data to produce realistic building occupancy patterns. The overall methodology can be subdivided into two steps: 1. Identification and grouping of households with similar daily occupancy profiles, using data mining clustering techniques; 2. Creation of probabilistic occupancy profiles using 'inverse function method'. The data from the model can be used as input to residential dwelling energy models that use occupancy time-series as inputs.
Funding Details: European Commission Horizon 2020
Type of material: Conference Publication
Publisher: IBPSA
Keywords: Occupant behaviourHeating demandResidential buildings
Other versions: http://www.buildingsimulation2017.org/
Language: en
Status of Item: Peer reviewed
Conference Details: Building Simulation 2017, San Francisco, CA, August 7-9 2017
Appears in Collections:Mechanical & Materials Engineering Research Collection
ERC Research Collection
Electrical and Electronic Engineering Research Collection

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