Turning faces into thermostats: Autonomous HVAC system could provide
more comfort with less energy

Date:
June 16, 2020
Source:
University of Michigan
Summary:
As lockdown requirements ease, COVID-19 is changing the way we
use indoor spaces. That presents challenges for those who manage
those spaces, from homes to offices and factories.



FULL STORY ==========================================================================
As lockdown requirements ease, COVID-19 is changing the way we use indoor spaces. That presents challenges for those who manage those spaces,
from homes to offices and factories.


==========================================================================
Not least among these challenges is heating and cooling, which
is the largest consumer of energy in American homes and commercial
buildings. There's a need for smarter, more flexible climate control that
keeps us comfortable without heating and cooling entire empty buildings.

Now, a group of researchers at the University of Michigan has developed
a solution that could provide more efficient, more personalized comfort, completely doing away with the wall-mounted thermostats we're accustomed
to.

Human Embodied Autonomous Thermostat, or "HEAT," is detailed in a study published in the July 2020 issue of Building and Environment.

The system pairs thermal cameras with three-dimensional video cameras
to measure whether occupants are hot or cold by tracking their facial temperature.

It then feeds the temperature data to a predictive model, which compares
it with information about occupants' thermal preferences.

Finally, the system determines the temperature that will keep the largest number of occupants comfortable with minimum energy expenditure. The new
study shows how the system can effectively and efficiently maintain the
comfort of 10 occupants in a lab setting.

"COVID presents a variety of new climate control challenges, as buildings
are occupied less consistently and people struggle to stay comfortable
while wearing masks and other protective gear," said project principal investigator and study co-author Carol Menassa, associate professor of
civil and environmental engineering.



========================================================================== "HEAT could provide an unobtrusive way to maximize comfort while using
less energy. The key innovation here is that we're able to measure comfort without requiring users to wear any detection devices and without the
need for a separate camera for each occupant." HEAT works a bit like
today's internet-enabled learning thermostats. When it's newly installed, occupants teach the system about their preferences by periodically
giving it feedback from their smartphones on a three-point scale: "too
hot," "too cold" or "comfortable." After a few days, HEAT learns their preferences and operates independently.

The research team is working with power company Southern Power to begin
testing HEAT in its Alabama offices, where test cameras will be mounted on tripods in the corners of rooms. Menassa explains that cameras would be
placed less obtrusively in a permanent installation. The cameras collect temperature data without identifying individuals, and all footage is
deleted immediately after processing, usually within a few seconds.

A second test, also with Southern Power, will place the system in an
Alabama community of newly constructed smart homes. The team estimates
that they could have a residential system on the market within the next
five years.

Facial temperature is a good predictor of comfort, Menassa said. When
we're too hot, the blood vessels expand to radiate additional heat,
raising facial temperature; when we're too cold, they constrict,
cooling the face. While earlier iterations of the system also used body temperature to predict comfort, they required users to wear wristbands
that measured body temperature directly, and to provide frequent feedback
about their comfort level.

"The cameras we're using are common and inexpensive, and the model works
very well in a residential context," said study co-author Vineet Kamat,
U- M professor of civil and environmental engineering, and electrical engineering and computer science. "Internet-enabled thermostats that
detect you and learn from you have sort of built a platform for the next
phase, where there's no visible thermostat at all." HEAT's predictive
model was built by U-M industrial operations and engineering associate professor Eunshin Byon, who is also an author on the study. She
believes that tweaks to the model could make the system useful in
applications beyond homes and offices -- in hospitals, for example,
where care providers struggle to stay comfortable under masks and other protective equipment.

"The COVID-19 pandemic requires nurses and other hospital workers to wear
a lot of protective gear, and they've struggled to stay comfortable in
the fast-faced hospital environment," Byon said. "The HEAT system could
be adapted to help them stay comfortable by adjusting room temperature or
even by signaling to them when they need to take a break." In partnership
with the U-M school of nursing, Menassa's research group has already
conducted a pilot study that explored how the system can be used to
provide personalized thermal comfort for nurses working in healthcare environments such as chemotherapy administration units.


========================================================================== Story Source: Materials provided by University_of_Michigan. Note:
Content may be edited for style and length.


========================================================================== Journal Reference:
1. Da Li, Carol C. Menassa, Vineet R. Kamat, Eunshin Byon. HEAT - Human
Embodied Autonomous Thermostat. Building and Environment, 2020;
178: 106879 DOI: 10.1016/j.buildenv.2020.106879 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/06/200616083353.htm

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