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2016 | OriginalPaper | Buchkapitel

Modelling and Detection of User Activity Patterns for Energy Saving in Buildings

verfasst von : Jose Luis Gomez Ortega, Liangxiu Han, Nicholas Bowring

Erschienen in: Emerging Trends and Advanced Technologies for Computational Intelligence

Verlag: Springer International Publishing

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Abstract

Recently, it has been noted that user behaviour can have a large impact on the final energy consumption in buildings. Through the combination of mathematical modelling and data from wireless ambient sensors, we can model human behaviour patterns and use the information to regulate building management systems (BMS) in order to achieve the best trade-off between user comfort and energy efficiency. Furthermore, streaming sensor data can be used to perform real-time classification. In this work, we have modelled user activity patterns using both offline and online learning approaches based on non-linear multi-class Support Vector Machines. We have conducted a comparison study with other machine learning approaches (i.e. Linear SVM, Hidden-Markov and K-nearest models). Experimental results show that our proposed approach outperforms the other methods for the scenarios evaluated in terms of accuracy and processing speed.

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Vorheriges Kapitel An Adaptive Multi Agent Service Discovery for Peer to Peer Cloud Services

Nächstes Kapitel A New Architecture to Guarantee QoS Using PSO in Fixed WiMAX Networks

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Note that in this work we have discretised our data in timeslices of 60 s. Therefore, we need to develop an online approach fast enough to be able to incorporate a new point and give an estimated class prediction within 1 min.

Our approach also differs from traditional online methods in which we do not apply any penalisation from a loss function, nonetheless this feature could be easily incorporated in the future versions of this algorithm.

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Titel: Modelling and Detection of User Activity Patterns for Energy Saving in Buildings
verfasst von: Jose Luis Gomez Ortega
Liangxiu Han
Nicholas Bowring
Verlag: Springer International Publishing
Buch: Emerging Trends and Advanced Technologies for Computational Intelligence
Print ISBN: 978-3-319-33351-9

Electronic ISBN: 978-3-319-33353-3

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-33353-3_9

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