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 m 2)building in Portugal.