Smart Industry 1/2016

How’s the Weather? Just ask Watson!
■ The Weather Company
the machine he is referencing, and
correlate recent maintenance to
identify the most likely source of the
vibration and then recommend an
action to reduce it.
Machine Learning processes data
automatically, monitors new data
and continuously and ranks data on
user interactions based on learned
priorities. Machine Learning can be
applied to any data coming from
devices and sensors to automatically
understand the current conditions,
what’s normal, expected
trends, properties to monitor, and
suggested actions when an issue
arises. For example, the platform can
monitor incoming data from fleet
equipment to learn both normal
and abnormal conditions, including
environment and production processes,
which are often unique to
each piece of equipment. Video and
Image Analytics which uses unstructured
data from video feeds and
image snapshots to identify scenes
and patterns. This knowledge can
be combined with machine data to
gain a greater understanding of past
events and emerging situations. For
example, video analytics monitoring
security cameras might note
the presence of a forklift infringing
on a restricted area, creating a minor
alert in the system; three days
later, an asset in that area begins to
exhibit decreased performance.
photo©: IBM
photo©: IBM
Power outages are mostly caused by the strains of
wind, rain, sleet, snow and ice. According to Brandon
Hertell, a certifi ed consulting meteorologist and a
manager at IBM’s Analytics division, an aging grid
and growing population makes the electrical grid
seven times more susceptible to failure. 70 percent
of storm-related outages to the U.S. are responsible
for between $20 and $55 bn in damages annually,
he estimates. In a world in which the climate is
changing, and major weather ‘events’ are becoming
more frequent, data suggests the number of
weather-related outages will continue to rise.
Today, utility managers examine multiple sources of
diverse and mostly unstructured data to fi gure out
what the weather will be like. Then, in a separate analysis,
they try to fi gure out what the impact will be. “If
they missed one seemingly innocent feature or data
point, like leaves still on trees”, Hertell says, “the whole
scenario changes.”
With the acquisition of The Weather Company, the
owner of one of America’s most popular weather
forecasting website weather.com, and with the help
of advanced analytics, IBM hopes to create order out
of chaos and in the process fi nd a way to tell us in
advance if it will rain or shine.
The Weather Company handles up to 26 billion
inquiries to its cloud-based services each day. Using
the data supplied by weather.com, IBM’s advanced
analytics aims at combining the weather, infrastructure
and historical impact information saving time
and allowing you to focus on proactively responding
to the storm.
Lead-time will be in days, not hours, allowing you to
make the appropriate calls for restoration resources.
Notifi cations can go out to employees so staffi ng
plans can be developed. Improved customer communications
will prepare them for potential outages. This
will improve their experience with the utility providers
and reduce regulatory scrutiny. If the weather forecast
changes or you want to run scenarios on different
storm strength, path, or timing it’s all possible with a
cloud-based solution. You can even perform signature
analyses and compare current weather to past events.
Weather will always be unpredictable, but the power
of cognitive insight, combined with analytics can help
utilities better prepare, and create new systems that
reason and learn over time. Combining weather data
with traditional business data from an unprecedented
number of Internet of Things enabled systems
and devices has the potential to signifi cantly impact
decision-making. Maybe we can’t change the weather,
but at least we will know what’s coming our way.
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photo©: IBM