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Title:	Clustering the occupant behavior in residential buildings: a method comparison
Abstract:	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. Eingruppierung des Nutzerverhaltens in Wohngebäuden: ein Methodenvergleich. Ziel dieser Arbeit ist es, mögliche Muster des Nutzerverhaltens in Wohngebäuden zu erkennen. Von einem Mehrfamiliengebäude wurden zunächst Daten, die auf das Nutzerverhalten schließen lassen, über einen Zeitraum von zwei Jahren erfasst. Zur Auswertung wurden zwei Clustering-Methoden verglichen: das Clustering von Zeitreihen und die Features Clustering-Methode (mittels k-Means-Algorithmus). Die genannten Methoden wurden hauptsächlich mit zwei Variablen (Fensteröffnung und Innentemperatur) durchgeführt, und mit Machine Learning-Methoden getestet. Die Ergebnisse weisen auf eine bessere Performance der Features Clustering-Methode gegenüber dem Clustering von Zeitreihen hin. Die Darstellung des Nutzerverhaltens durch Feature Clustering wird in einer zukünftigen Arbeit als Teil einer Optimierung der Regelungsstrategien von Lüftungsanlagen genutzt.
Source:	Bauphysik 40 (2018), No. 6
Page/s:	427-433
Language of Publication:	German

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