Outcomes, Insights and Best Practices from IIC Testbeds: Deep Learning Facility Tesbed
In addition to a focus on energy efficiency,
SAS aligns with Toshiba’s mission to focus on
equipment, predicting equipment failures
and finding equipment malfunctions—a
difficult mission analogous to finding a
needle in a haystack. IIC’s Deep Learning
Facility Testbed provides the avenue for
these companies to fulfill this vision.
establish a distributed deep-learning system
platform.
There are areas of experimentation in the
Deep Learning Facility Testbed. Modern
buildings have many IoT sensor systems for
various systems within the building. Sensors
monitor large-asset maintenance, granular
energy efficiency and occupant usage. AI and
Deep Learning can use all of the data from
these individual systems to achieve higher-
order
objectives
such
as
energy
sustainability and occupant satisfaction. In addition to being deployed at the Toshiba
Smart Community Center in Kawasaki, the
Deep Learning Facility Testbed is also
implemented at the SAS Smart Campus in
Cary, North Carolina, USA. This corporate
headquarters has 24 buildings, ranging from
two months to 20 years old. Though
primarily an office campus, there are also
daycare centers, fitness facilities and
restaurants. While the Toshiba Kawasaki
building
employs
greenfield
instrumentation, the SAS site also has
brownfield. The testbed mostly covers
buildings created within the last six years,
but there are also projects underway to
retrofit some of its older buildings.
The Deep Learning Facility Testbed’s Phase 1
target at the Toshiba Smart Community
Center is sensor-anomaly detection. The
building facility management team is
concerned with the shortage of people to
monitor, control and manage all the building
assets, but AI can support these tasks
automatically. Deep-learning technology
assists the management team in detecting
anomalies in sensors and assets and
analyzing data from 30,000 data points. SAS uses neural networks on all of the
building data. The number of different
measurements from the SAS buildings were
similar to that of the Toshiba Kawasaki
building. Neural networks help create a
model that understands building energy
usage for monitoring the building to
determine whether it is performing as
expected in accordance with its design and
understanding where and why deviations
from those expectations may occur.
Another area of experimentation is trying to
determine what is computationally feasible
and how to apply various high-end
technologies
to
maximize
those
computation capabilities. For example,
autoencoders have been used for anomaly
detection. Deep learning needs a substantial
amount of time for computing resources,
and Dell’s rapid storage system helps The testbed plans to publish implementation
lessons learned, best practices and use case
results. The algorithmic knowledge and
innovations discovered through the
implementations of this testbed are also
intended to be applied commercially by the
participating companies.
IIC Journal of Innovation
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