begins to learn. Over time, it will widen dead bands
in zones with little feedback, especially unoccupied
zones like elevator lobbies and copy rooms. In areas
where feedback is regular, it will optimize to a narrow range of temperatures based on worker preferences – and it will adjust those ranges as preferences
change over the course of days, weeks and seasons.
It also learns to widen bands on days when zones are
unoccupied, like weekends and holidays.
At one of Building Robotics’ first installation sites,
the client measured a 23% reduction in energy use for
heating and cooling over the six-month pilot period.
And, in a development that was perhaps even more
appealing to the facility management team, hot and
cold calls were reduced to zero. “The complaints are
a big headache for facility managers, so they didn’t
mind giving occupants some freedom if it made them
happier,” notes Baker.
For her part, Baker has gained new insight as well.
For instance, people in warmer climates tend to gravitate toward an optimal temperature that is closer to
the high end of the range set by facility management.
Users also generally want their space warmer in the
morning and cooler in the afternoon.
“People are unpredictable,” she says. “The notion
behind Comfy is to embrace that unpredictability and
give them some control, while at the same time not
over-conditioning the space.”
A Few Degrees of Freedom
The usefulness of BAS data doesn’t stop at tracking
energy consumption to learn from the data feed. In
fact, some systems flip that paradigm and learn from
their human operators.
“Machine learning is a big part of the story. There
is a lot of repetition and pattern recognition involved
in looking at data, and it just isn’t scalable,” says Lindsay Baker, VP of business development for Building
Robotics, a building controls company that grew out
of UC Berkeley’s computer science department. The
firm’s breakthrough product is Comfy, an interactive
temperature control system built on a machine-learn-ing platform.
Comfy works in a counterintuitive way – it gives
individual workers a measure of control over the
temperature in their space. Workers are given access
to a web portal or iPhone app that allows them to dispatch one of three messages: “Warm my space,” “I’m
comfortable,” or “Cool my space.” This control, which
is connected to the BAS on the back end, responds
immediately to the request.
“If someone wants cooler space, that person’s
zone will get about 10 minutes of cool air,” Baker ex-
plains. “People are usually amazed at the response,
which makes them feel like the building is listening
Critically, the system does not immediately alter
the set points. Instead, it assimilates the feedback and continued
BIG DATA OR BIG BROTHER?
What about privacy and security? It is a familiar question to anyone who works in data analytics. Whether that data relates to workstation utilization or equipment performance, the concerns are real. Analytics providers seek to allay these concerns by describing the privacy and security solutions they have developed.
“We spent some time working through security,” says Lindsay Baker of Building Robotics, noting that the
interactive nature of her company’s product made it doubly important to reassure users that their information
would be private and secure. “The requests that come from users are anonymous, and what gets transmitted
does not directly control the system.”
Abintra, which has several clients in the European Union (EU), has also had to think through privacy concerns.
“In some parts of the EU, it is actually illegal to measure worker performance, including workstation occu-
pancy,” says Tony Booty. “To address this, we have installed the sensors in such a way that they are randomly
scrambled within a certain neighborhood. We couldn’t identify the individuals even if we wanted to.”
Aquicore’s Logan Soya relates that controlled centralization is one security feature of his firm’s devices.
“Even though it’s a cloud-based platform, the sensors aren’t individually web-enabled,” Soya explains. “In-
stead, the data is securely relayed to a gateway point, at which point it is encrypted in the same way banking
information is before being sent to the cloud. Not only is this a best practice, but there is value to us in separat-
ing the data collection from the analytics platform.”