Evaluating the Performance of HVAC Optimal Control Based on Real-time Floor-by-Floor Occupancy Data
Meeting aggressive decarbonization goals requires radical advancements that reduce
existing buildings’ carbon footprint. New smart building technologies that offer continuous
dynamic optimization of commercial Heating, Ventilation, and Air Conditioning (HVAC)
control hold promise to advance building operations for decarbonization, efficiency, and flexible
control. Typical HVAC control sequences are designed to condition spaces over a fixed schedule
to meet space temperature setpoints. By incorporating occupancy information into HVAC
control, space conditioning can be delivered more efficiently and adjust to changes in occupancy.
This paper illustrates the results from a field evaluation of a cloud-based building operation
platform that was installed in an office building. In the study, 22 occupancy counters were
installed (two at each floor) as a part of the platform to measure floor-by-floor building
occupancy in real-time. The platform used these occupancy data along with thermal modeling,
and machine learning algorithms to implement optimal start-up, shut down, and intra-day fan
speed adjustment to the air handling units (AHUs) for each floor in the pandemic.
This paper presents how the technology is implemented, the energy savings performance, and how
occupancy information can be used to support executing dynamic operation during COVID-19
time period. The technology reduced weekday AHU run times by over three hours and reduced
fan speed by more than ten percent during lunchtime in the Pandemic period. The simulated
savings results are presented and compared with another similar study. The assessment results
provide valuable insights to help end-users and industry partners understand real-world
performance of occupancy based control technologies and reduce technical risks.