Implementation of an Adaptive Occupancy and Building Learning Temperature Setback Algorithm

摘要

Due to the challenges in predicting the recurring occupancy patterns and the length of nighttime temperature setback to daytime setpoint transition periods in office buildings, operators have been challenged to choose conservatively short temperature setback periods. In recognition of these challenges, a self-adaptive control algorithm that can learn both the recurring occupancy patterns and the parameters of a model predicting the indoor temperature response was implemented in a southwest-facing shared office space in Ottawa, Canada. Results from this implementation indicate that the parameters describing the occupancy, building, and terminal HVAC system characteristics converge to stable andphysically meaningful values in less than two weeks. The control algorithm was also implemented in the energy management system (EMS) application of the building performance simulation (BPS) tool EnergyPlus to adapt the temperature setback schedules of a BPS model of the monitored office. The comfort and annual heating/cooling load implications of 100 different static setback strategies were compared with the adaptive setback periods selected by the control algorithm. Simulation-based results indicate that, with this control algorithm, the frequency of occupant overrides to the thermostat setpoints was the same as it was with a static temperature setback schedule that covers 55% of the year with 15 to 20% lower annual cooling loads and 8 to 10% lower annual heating loads.