Paper Conference

Proceedings of Building Simulation 2017: 15th Conference of IBPSA

     

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

Giuseppina Buttitta1, Olivier Neu2, Will Turner3, Donal Finn3
1Electricity Research Centre, University College Dublin, Dublin, Ireland
2School of Electric and Electronic Engineering, University College Dublin, Dublin, Ireland
3School of Mechanical and Materials Engineering, University College Dublin, Dublin, Ireland


DOI: https://doi.org/10.26868/25222708.2017.478
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 influenced 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.
Pages: 1788 - 1797
Paper:
BS2017_478