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December 12, 2012

Utilities Knife through Big Data

Ian Armas Foster

An increasing worldwide focus on energy efficiency and slowing the effects of global climate change has prompted new advancements in smart energy technology. But devices such as smart meters and monitors that constantly check power conditions end up collecting a massive amount of data that could overwhelm utilities if they are unprepared.

This report from the Energy Collective outlines three distinct areas in which big data is playing a significant role in prompting more intelligent energy management: smart metering, designing and maintaining wind farms, and handling of the nation’s power network.

Meters that are typically read just once a month for billing and recording purposes are on their way out. In to replace them are smart meters—devices that relay information to the utility company several times a day. Further, they measure more than just the power output; they also collect and aggregate data trends that could help companies study circumstances around peak usage. These trends could then be relayed to users, allowing them to perform energy intensive tasks when the demand is relatively low.

This overall sharing of information could help utilities and users to work together to increase efficiency and reduce waste. However, the data requirements are significant, with smart meters providing at least a 300x increase in information intake. A meter that sends data every two hours will give out readings twelve times a day over thirty days versus just once a month.

Storing and processing big data is becoming paramount to utility companies for record-keeping and maintenance (more on that later). But it is also important to windmill manufacturers for a similar purpose: optimization.

Farms of windmills dot the plains of Illinois as one drives up to Chicago. It is one of the most beautiful sights to an environmentalist. The principle behind their placement seems simple enough: plop them down on the windy fields of the Midwest, however, a lot more goes into it than that, including a significant amount of big data analytics.

Vestas, a sizable manufacturer of windmills, is currently using IBM’s supercomputing power to help optimize both the location and design of their turbines. The company, according to the report, is en route to taking in 20 petabytes of information on various weather patterns. Due to advancements in big query run-time, Vestas is allowed to run optimization simulations on that data and have them completed within a few hours as opposed to a few months.

Of course, those windmills and smart meters need to be maintained. Repairing a damaged meter or turbine is unnecessarily expensive in that organizations can use predictive analytics from big data to report problems before they happen. Intuitively, if one can identify an impending issue before that issue causes a system failure, the system is repaired quicker and more cheaply.

With the new devices designed to monitor and deliver reports on the millions of miles of cables and cords that cross the nation, the data demands are enormous. The report estimates that ten billion devices will be employed. However, there is great incentive to interpret that data in order to prevent or limit outages, along with predicting weather patterns such that companies can prepare accordingly for events like the recent Superstorm that afflicted the Northeast United States, and New York in particular.

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