Occupant location prediction using association rule mining

HVAC systems are significant consumers of energy, however building management systems do not typically operate them in accordance with occupant movements. Due to the delayed response of HVAC systems, prediction of occupant locations is necessary to maximize energy efficiency. In this paper we present an approach to occupant location prediction based on association rule mining, which allows prediction based on historical occupant movements and any available real time information. We show how association rule mining can be adapted for occupant prediction and show the results of applying this approach on simulated and real occupants.