ASHRAE Journal - May 2021 - 34

TECHNICAL FEATURE

prompt occupants to register feedback about their thermal comfort.12

Occupant-Centric Indoor Climate Control Metrics
Due to the slow thermal response of buildings, instantaneous occupancy and occupant preference data
cannot be used directly in HVAC controls such as temperature setbacks;13 data need to be converted into OCC
metrics (Figure 2).
The two OCC metrics that can be developed from
building-level presence/absence data (IG 1) are the
earliest expected arrival and the latest expected departure times in a building/system. This information can
be used in scheduling AHU start/stop times. Based on
building-level occupant count data (IG 3), a third building-level metric can be calculated: the highest expected
number of occupants in a building. Given the diversity
of individual work schedules, at any given time only a
fraction of the occupants is expected to be in the building. An AHU's outdoor air damper can be controlled
based on instantaneous occupant count detections in
real time, or the highest expected number of occupants
on a given day can be estimated conservatively and used
to determine a minimum outdoor air damper position
setpoint.
With zone-level presence/absence data (IG 2), three
zone-level OCC metrics can be calculated: the earliest expected arrival, latest expected arrival and latest
expected departure times in a zone/room. The earliest
expected arrival and latest expected departure times
can be used in temperature setback scheduling on occupied weekdays. The latest expected arrival times can be
used quite effectively in midday temperature setback
scheduling in zones/rooms with frequent unoccupied
weekdays: if the current time exceeds the latest expected
arrival time of a zone/room and the zone/room remains
vacant, then it can be assumed that the zone/room will
remain vacant until the next day; thus, temperature setback can begin.
Based on zone-level occupant count data (IG 4), a
fourth zone-level OCC metric can be calculated: the
highest expected number of occupants in a zone. In
multi-occupant thermal zones, this metric can be used
to modulate VAV terminal unit supply airflow setpoint.
Ability to fine-tune ventilation based on occupancy data
at the zone level will not only achieve energy savings
but improve the controllability of occupant-induced
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FIGURE 2 Building- and zone-level OCC metrics that can be determined from different IGs of occupant data.

Building-Level OCC Metrics

Occupant
IGs

Zone-Level OCC Metrics

1
* Earliest expected arrival time to
a building
* Latest expected departure time
from a building

2

* Highest number of occupants in
a building

4

* Preferred thermal conditions in a
building

3

5
6

* Earliest expected arrival time to a zone
* Latest expected arrival time to a zone
* Latest expected departure time from
a zone
* Highest number of occupants in a zone

* Preferred thermal conditions in
a zone

contaminants (e.g., infectious aerosols, odor, CO2) at the
zone level. However, it is an inherent deficiency of multizone VAV AHU configuration that diversity in thermal
loads among VAV zones served by the same AHU affects
how outdoor air is distributed among these zones.
Hence, zone-level occupant count information does
not guarantee proportionality between instantaneous
occupant counts and ventilation rates. HVAC configurations such as dedicated outdoor air systems coupled
with radiant heating and cooling represent a compelling
alternative to VAV AHUs, as they improve adaptability to
variable occupancy levels.
Occupant preference data at the building (IG 5) and
zone level (IG 6) can be combined with concurrent IEQ
data (e.g., indoor temperature, humidity) to compute
two more OCC metrics: preferred thermal conditions at
the building and zone levels, respectively. These metrics
can be used to determine both setpoints that minimize
the risk of occupant overrides or complaints.

Case Study to Discuss Energy and IEQ Benefits
This section presents an analysis based on occupancy,
thermostat keypress and indoor temperature data collected from 37 private offices in an academic office
building in Ottawa, Canada, that was occupied by fulltime faculty and staff. The occupancy data were generated by the built-in PIR motion detectors of the thermostats and collected at 15 minute intervals over a year in
2017 by the building's energy management system. The
offices were served by multizone VAV AHU systems. The
zones were equipped with VAV terminals with reheat
and ceiling-mounted hydronic heaters. Occupants could
change temperature setpoints temporarily (until midnight) up to ±2°C (±4°F). We used the data to compute
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