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

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Show simple item record Buttitta, Giuseppina Neu, Olivier Turner, William J. N. Finn, Donal 2018-02-12T13:37:20Z 2018-02-12T13:37:20Z 2017-08-09
dc.description Building Simulation 2017, San Francisco, CA, August 7-9 2017 en
dc.description.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. en
dc.description.sponsorship European Commission Horizon 2020 en
dc.language.iso en en
dc.publisher IBPSA en
dc.subject Occupant behaviour en
dc.subject Heating demand en
dc.subject Residential buildings en
dc.title Modelling Household Occupancy Profiles using Data Mining Clustering Techniques on Time Use Data en
dc.type Conference Publication en
dc.status Peer reviewed en 2018-06-19
dc.neeo.contributor Buttitta|Giuseppina|aut|
dc.neeo.contributor Neu|Olivier|aut|
dc.neeo.contributor Turner|William J. N.|aut|
dc.neeo.contributor Finn|Donal|aut|
dc.description.admin Check for published version, proceedings en 2017-12-18

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