Database Reference
In-Depth Information
Today, many utilities are moving to smart meters and grids as part of long-
range plans to ensure a reliable energy supply, incorporate distributed genera-
tion resources, and enable customers to have more control over their energy
use. Many are deploying smart meter systems as a first step, which presents an
immediate technical challenge: Going from one meter reading a month to
smart meter readings every 15 minutes works out to 96 million reads per day
for every million meters: a 3,000-fold increase in data collection rates! As you
can imagine, such rates could be crippling if not properly managed.
There's an upside, of course. The additional data opens up new opportu-
nities, enabling energy companies to do things they never could do before.
Data gathered from smart meters can provide a better understanding of cus-
tomer segmentation and behavior, and of how pricing influences usage—but
only
if
companies have the ability to use such data. For example, time-of-use
pricing encourages cost-savvy energy consumers to run their laundry facili-
ties, air conditioners, and dishwashers at off-peak times. But the opportuni-
ties don't end there. With the additional information that's available from
smart meters and smart grids, it's possible to transform and dramatically
improve the efficiency of electrical generation and scheduling.
Smart meters are smart because they can communicate, not only with the
consumer about electricity usage and pricing, but also with the utility pro-
vider about fluctuations in power or the precise location of outages. Smart
meters are generating a wealth of new information that's fundamentally
changing the way that utility companies interact with their customers.
What about the advent of the
prosumer
, a new consumer class that's also a
producer. Prosumers generate power through solar panels and sell it back to
the grid; this too has ripple effects across the supply chain. Using predictive
analytics on their data, companies can make a wide range of forecasts, such as
excess energy calculations with sell and transmittal considerations, typical fail-
ure points and grid downtime locations, and which clients are likely to feed
energy back to the grid and when they are likely to do so.
Now consider the additional impact of social media. A social layer on top of
an instrumented and interconnected world generates a massive amount of
data too. This data is more complex, because most of it is unstructured (images,
Twitter tweets, Facebook posts, micro-blog commentaries, and so on). If you
eat Frito-Lay SunChips, you might remember its move to the world's first
biodegradable environmentally friendly chip bag; you might also remember
