LAUSR

Abstract Even though it has been widely accepted that occupancy is one of the most important factors impacting energy use of HVAC systems, how occupancy is associated with heating/cooling loads for sustained and maximum energy efficiency in multi-zone buildings is still not well understood. This study analyses the impact of occupancy on heating/cooling loads at the building level using occupancy state transitions. A data-driven approach is developed applying an enhanced Variable Neighborhood Search algorithm to determine setpoint controls for individual zones and to minimize the heating/cooling loads based on occupancy state transitions. Simulations are used to compare the loads after implementing the proposed approach with the baseline control to quantify the impacts. Based on the results, the optimal combinations of setpoint/setback schedules and distances for each zone were identified to minimize heating/cooling loads at the building level. The convergence of the search was not influenced by different occupancy assignments or initial solutions, and there was no random solution that could outperform the proposed approach to reduce heating/cooling loads based on occupancy transitions. A minimum of 10.4% and a maximum of 28.3% load reduction were achieved in the case study building, compared to the baseline control.