Learning occupancy in single person offices with mixtures of multi-lag Markov chains

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Manna, Carlo
Fay, Damien
Brown, Kenneth N.
Wilson, Nic
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IEEE Computer Society
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The problem of real-time occupancy forecastingfor single person offices is critical for energy efficient buildings which use predictive control techniques. Due to the highly uncertain nature of occupancy dynamics, the modeling and prediction of occupancy is a challenging problem. This paper proposes an algorithm for learning and predicting single occupant presence in office buildings, by considering the occupant behaviour as an ensemble of multiple Markov models at different time lags. This model has been tested using real occupancy data collected from PIR sensors installed in three different buildings and compared with state of the art methods, reducing the error rate by on average 5% over the best comparator method.
Markov chains , Occupancy prediction , Building control
MANNA, C., FAY, D., BROWN, K. N. & WILSON, N. 2013. Learning occupancy in single person offices with mixtures of multi-lag Markov chains. In: Proceedings 25th International Conference on Tools with Artificial Intelligence ICTAI 2013. Washington DC, USA, 4-6 Nov. Los Alamitos, California: IEEE Computer Society, pp. 151-158.
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