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Modelling Household Occupancy Profiles using Data Mining Clustering Techniques on Time Use Data
Date Issued
2017-08-09
Date Available
2018-02-12T13:37:20Z
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.
Sponsorship
European Commission Horizon 2020
Type of Material
Conference Publication
Language
English
Status of Item
Peer reviewed
Part of
Barnaby, C.S. and Wetter, M. (eds.). Building Simulation 2017
Conference Details
Building Simulation 2017, San Francisco, CA, August 7-9 2017
ISBN
978-1-7750520-0-5
ISSN
2522-2708
This item is made available under a Creative Commons License
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BS2017_Occupants_01_1_1478_Buttitta_2017-05-12_03-40_a.pdf
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1.35 MB
Format
Owning collection
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