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A good occupancy prediction model requires enough input data pertinent to the occupants’ space utilization patterns. However, most of the occupancy detection systems cannot provide this detailed information, which reduces their practicality for open-plan offices. Therefore, there is a need to use proper sensing techniques to distinguish between different occupants in open-plan offices when detecting occupancy patterns. In this study, the probabilistic occupancy modelling has been further enhanced using inhomogeneous Markov chain prediction model based on data collected by a Real Time Locating System (RTLS). The comparison between the occupancy profiles resulting from the prediction model and the actual profiles showed that the prediction model was able to capture the behaviour of occupants. An adaptive probabilistic occupancy prediction model, which distinguishes the temporal behaviour of different occupants within an open-plan office, allows for the application of occupancy-centred local control strategies.

A Generalized Inhomogeneous Markov Chain Occupancy Model For Open-Plan Offices Using Real Time Locating System Data