Journal
6TH INTERNATIONAL BUILDING PHYSICS CONFERENCE (IBPC 2015)
Volume 78, Issue -, Pages 585-590Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.egypro.2015.11.022
Keywords
data mining; occupant behavior; office building; window operation; occupancy patterns
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Literature studies confirm occupant behavior is setting the direction for contemporary researches aiming to bridge the gap between predicted and actual energy performance of sustainable buildings. Using the Knowledge Discovery in Database (KDD) methodology, two data mining learning processes are proposed to extrapolate office occupancy and windows' operation behavioral patterns from a two-years data set of 16 offices in a natural ventilated office building. Clustering procedures, decision tree models and rule induction algorithms are employed to obtain association rules segmenting the building occupants into working user profiles, which can be further implemented as occupant behavior advanced-inputs into building energy simulations. (C) 2015 The Authors. Published by Elsevier Ltd.
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