Flávia Dias Casagrande
Department of Electronic Engineering, OsloMet - Oslo Metropolitan University, Norway
Evi Zouganeli
Department of Electronic Engineering, OsloMet - Oslo Metropolitan University, Norway
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http://dx.doi.org/10.3384/ecp18153236Published in: Proceedings of The 59th Conference on Simulation and Modelling (SIMS 59), 26-28 September 2018, Oslo Metropolitan University, Norway
Linköping Electronic Conference Proceedings 153:33, p. 236-242
Published: 2018-11-19
ISBN: 978-91-7685-494-5
ISSN: 1650-3686 (print), 1650-3740 (online)
In this paper we present event anticipation and prediction of sensor data in a smart home environment with a limited number of sensors. Data is collected from a real home with one resident. We apply two state-of-the-art Markovbased prediction algorithms Active LeZi and SPEED and analyse their performance with respect to a number of parameters, including the size of the training and testing set, the size of the prediction window, and the number of sensors. The model is built based on a training dataset and subsequently tested on a separate test dataset. An accuracy of 75% is achieved when using SPEED while 53% is achieved when using Active LeZi.
