Predicting Occupant Locations Using Association Rule Mining

Heating, ventilation, air conditioning (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 two approaches to occupant location prediction based on association rule mining which allow prediction based on historical occupant movements and any available real time information, or based on recent occupant movements. We show how association rule mining can be adapted for occupant prediction and evaluate both approaches against existing approaches on two sets of real occupants.