A context-aware method for building occupancy prediction

• Occupancy prediction based on historical data and current context.
• Development of 2 different algorithmic approaches based on Markov models.
• Experimentation based on real-life accurate data for 3 different types of spaces.
• Sufficient results giving a significant improvement compared to existing approaches.

In this paper a building occupancy prediction method is presented, which is based on the spatio-temporal analysis of historical data (occupancy modelling) and further relies heavily on current contextual information, being therefore suitable for providing real-time prediction. Two different algorithmic approaches are proposed, based on Markov models, revealing how context awareness adds the capability of rapidly adjusting to current conditions and capturing unexpected events, as opposed to capturing only typical occupancy fluctuation expected on a regular basis. Both proposed approaches are evaluated against accurate real-life data collected from a tertiary building, achieving notable results which outperform currently used methods.

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