Occupancy Pattern Extraction and Prediction in an Inhabited Intelligent Environment Using NARX Networks

In this paper, occupancy pattern extraction and prediction in an intelligent inhabited environment is addressed. The results of this research will help elderly people to live independently in their own home longer and help them in case of an emergency. Using a wireless sensor network system, daily behavioral patterns of the occupant are extracted. This information is then used to build a behavioral model of the occupant which ultimately is used to predict the future values representing the expected occupancy and other activities. The occupancy signal is represented by a long sequence of binary series indicating presence or absence of the occupant in a specific area. It is essential to convert this series of binary data into a more flexible and efficient format before it is applied for any further analysis and prediction. After converting the occupancy binary signals, the prediction model is built through a recurrent dynamic network, with feedback connections enclosing several layers of a Nonlinear Autoregressive netwoRk with eXogenous inputs (NARX) network. The results reported here shows that NARX provide better prediction results than conventional recurrent neural networks such as Elman networks. The case study reported here is based on a one bedroom flat with a single occupant.