Development and evaluation of HVAC operation schedule detection algorithm

Abstract

Small- and medium-sized (<5,000 m2 or <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 the systems in these buildings 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. Because there is no monitoring infrastructure in these buildings, deviation of actual heating, ventilation and air conditioning (HVAC) set points and schedules from expected cannot be easily inferred or detected. In this paper, a new methodology is proposed to detect HVAC operation schedules using a symbolic aggregate approximation (SAX) technique. This new method only requires a single temperature measurement (zone temperature) to infer the HVAC schedule. The method was tested and validated using field data from a number of RTUs from six buildings in different climate locations. The confusion metric was used to evaluate how well the method is able to identify the operating schedule of RTUs. Overall, the schedule detection method was successful in detecting the daily operating schedule of RTUs under summer (hot) and winter (cold) weather conditions when energy use is highest or when the RTUs are cycling ON/OFF often. The method can also distinguish different schedules and generate clusters of HVAC operating schedules.