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Clustering the occupant behavior in residential buildings: a method comparison
Author(s) -
Carbonare Nicolás,
Pflug Thibault,
Wagner Andreas
Publication year - 2018
Publication title -
bauphysik
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.166
H-Index - 9
eISSN - 1437-0980
pISSN - 0171-5445
DOI - 10.1002/bapi.201800023
Subject(s) - cluster analysis , computer science , window (computing) , artificial intelligence , machine learning , engineering , operating system
The aim of this paper is to investigate possible patterns of the occupant behavior in residential buildings. Measurements were taken in multi‐family buildings where several occupant‐related variables were recorded. We chose and compared two different clustering methods: whole time series and features clustering (k‐means algorithm). The mentioned methods were performed selecting two variables (window opening and indoor temperature) and tested with supervised learning methods. Results suggest that features clustering can perform better than whole time series. The representation of the occupant behavior through features is meant to be applied in future work regarding the optimization of control strategies in ventilation systems.