Development and Evaluation of HVAC Operation Schedule Detection Algorithm

Abstract

Small- and medium-sized (<50,000 sf) commercial buildings (SMBs) represent over 94% of the U.S. commercial building stock and consume over 1018 kJ (~1015 Btus) of total site energy. Many of these buildings use rudimentary controls that are mostly manual, with limited scheduling capability and no monitoring or failure management. Therefore, many of them are operated inefficiently and consume excess energy. SMBs typically use packaged rooftop units (RTUs) that are controlled by an individual thermostat. Studies have shown that managing set points and schedules of the RTUs will result in up to 20% energy/cost savings. This paper is focused only on the detection of the occupancy schedule using the zone temperature. The paper describes the methodology to detect the occupancy schedule, the metric used to validate the algorithm and the validation results. The algorithm has been tested and validated using field data from a number of RTUs from six buildings in different climate locations. Overall, the algorithm was successful in detecting the occupancy schedule using a symbolic aggregate approximation (SAX) technique. The results showed that the occupancy schedule detection algorithm using the SAX method performs well under summer (hot) and winter (cold) weather conditions when energy use is the highest. The algorithm can also distinguish different schedules and generate clusters of occupancy scheduling based on zone temperature measurements alone.