Clustering and motif identification for occupancy-centric control of an air handling unit

Abstract Commercial and institutional buildings often operate at a fraction of their full occupancy, yet ventilation is frequently provided assuming maximum occupancy during working hours. Forecasting building occupancy can improve the operation of system level equipment by reducing chronic overventilation. However, developing actionable occupancy forecasting techniques for controls applications is subject to technological, security, and financial barriers. This paper proposes an occupancy forecasting technique that can be implemented in a controller given these constraints. Seven months of Wi-Fi, plug-in equipment and lighting electricity data were collected from an academic office building. Using Wi-Fi data, the building’s mean occupancy was demonstrated to be less than 25% of the 1000-person maximum. Representative daily occupancy profiles were produced by different clustering techniques. A classification tree was developed to determine motif occupancy profiles for day-ahead forecasting. Corresponding electrical profiles were taken to see if they followed the same trend as the occupancy profiles; 84.5% of days shared the same trends. The electrical data were fed through the classification tree, with a successful occupancy classification rate of 70.4% and error of 47 ± 69 persons at 95% confidence. A controls implementation for adaptive outdoor air damper actuation based on this technique is proposed for future work.

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