CO2 Sensor Occupancy Detection
A group of researchers at the University of California in
Berkley have designed an algorithm that can count the number of people
in a room when only the CO2 level is known.
The study, titled “
Sensing by Proxy: Occupancy Detection Based on Indoor CO2 Concentration”
used SenseAir’s K-30 carbon dioxide sensor module for occupancy
detection. The "Sensing by Proxy" model is more accurate than
previously used machine learning models, and could be used to improve
the efficiency of Demand-Controlled Ventilation systems (DCV) currently
in use.
DCV works on the principle that you can save energy by heating,
cooling or adding fresh air to a room only when it is needed. While
heating and cooling can be controlled with a thermostat, the amount of
fresh air that should be added to a room can be controlled by a carbon
dioxide level transmitter. When CO2 levels go up, fresh air is added
until the CO2 levels return to normal (typically 10% or less of the
background CO2 levels).
While
CO2 transmitters
in a DCV system are good at monitoring CO2 levels, they do not tell
you the number of occupants in a room. This is important because
indoor air quality models specify the optimum fresh air exchange rate
per person. If you don’t know how many people are in a room, all the
system can do is pump in fresh air until the CO2 level drops.
This is where Sensing by Proxy comes in. Using the K-30
sensor’s CO2 level data as the only input, the researchers were able
to create a model “that captures the spatial and temporal features of
the system and links unobserved human emission to proxy measurements
of CO2 concentrations.”
The algorithm they developed was tested in both controlled and
field experiments, and resulted in an error rate of 0.6 fractional
persons as compared to 1.2 fractional persons by the best alternative
strategy. The algorithm is also better at measuring changes, such as
occupants entering and leaving a meeting. In addition, it does not
require more costly Pyroelectric infrared (PIR) or Ultrasonic sensors
used for current occupancy detection systems.
The algorithm is one of a growing number of papers that test
the use of CO2 levels for occupancy detection. For example, Chaoyang
Jiang and others in a paper titled “Indoor occupancy estimation from
carbon dioxide” were able to estimate occupancy in an office with 24
cubicles. They counted occupants accurately 94 percent of the time
within a tolerance of four occupants.
While DCV is initially more expensive to install, improvements
like this could be used to further reduce the long-term energy costs
in DCV controlled buildings.
