ZEMCH 2015 - International Conference Proceedings | Page 243
International Conference | Bari - Lecce, Italy
Session IV
MODELLING OCCUPANT ACTIVITY PATTERNS FOR
ENERGY SAVING IN BUIDINGS USING MACHINELEARNING APPROACHES
Jose Luis Gomez Ortega1 & Liangxiu Han1
1School Of Computing, Mathematics & Digital Technology, Manchester Metropolitan University, UK,
[email protected]
2School Of Computing, Mathematics & Digital Technology, Manchester Metropolitan University, UK,
[email protected]
Abstract
Since occupants play an important role in the energy consumption in buildings, it is crucial
to develop accurate models of occupant behaviour patterns for the automation of building
management systems. With the advancement in information and communication technology
(ICT), machine-learning technologies have been used for accurately modelling and learning
occupant behaviour patterns based on environment sensing. In this work, we have developed
a pattern recognition model based on non-linear multiclass Support Vector Machine classification approach, to accurately detect occupant behaviour patterns by modelling human activities of daily living (ADL). To evaluate the proposed model, we have selected multiple public
datasets collected by different teams from diverse ambient sensors (i.e. motion, contact or
pressure sensors). Two different data pre-processing techniques (i.e. Timeslice approach (TA)
and chunk data (CDA)) have been developed for facilitating the construction of the models.
Furthermore, we have also compared our model with other machine learning techniques (i.e.
Hidden-Markov Model (HMM) and k-Nearest Neighbours (KNN)). The experimental results
have shown that the proposed SVM-based model outperforms the other methods in terms
of accuracy in three different scenarios for Dataset1 using the TA method and for the second
dataset evaluated, combinin g CDA data pre-processing and SVM approaches, the accuracy is
higher than other methods such as HMM and KNN.
Keywords
machine learning, SVM, mathematical modelling, energy efficiency, sensors.
241