Rule-based scheduling of air conditioning using occupancy forecasting

Heating, ventilation and air conditioning systems represent considerable potential for energy savings, which can be realized through intelligent occupancy-centered control strategies. In this work, both supervised and unsupervised algorithms to forecast occupancy are proposed with the highest accuracies of 98.3% and 97.6%, respectively. Building on their output, a rule-based air conditioning scheduling technique is developed. As an example, a potential of 15.4% of energy savings is calculated using a dataset collected in a mid-size (4000 m2) building in Portugal.