Smarter is: Putting renewable energy on the map

Vestas Wind Systems tunes wind farm and turbine placement with InfoSphere BigInsights

Vestas Wind Systems of Aarhus, Denmark uses IBM InfoSphere BigInsights to analyze location-specific data, such as wind speed, temperature, humidity, atmospheric pressure, and precipitation, in order to predict wind farm performance. This helps them to install wind turbines at optimal locations, converting energy at a modest price, while leaving a minimal carbon footprint.

This article was originally published in IBM Data magazine.

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David Geer, Technology journalist, Consultant

David Geer is an Ohio-based technology journalist.



21 October 2011

Also available in Chinese

Cheaper electricity, cleaner air, and peace of mind await us, just by harnessing the ever-abundant wind. Wind turbines capture and convert energy at a modest price (four to six cents per kilowatt-hour), while leaving a minimal carbon footprint1.But formidable capital costs loom; these power generators cost millions of dollars and may endure just 20 to 30 years. If power companies don’t install them in optimal locations (where they can be operated continuously and at maximum efficiency), wind farms may not generate enough energy over a long enough period to be worth the investment.

Vestas Wind Systems of Aarhus, Denmark, with more than 44,000 wind turbines in 67 countries and on land and sea, has found a way to remove some of that risk. For best turbine placement, the company analyzes location-specific data, such as wind speed, temperature, humidity, atmospheric pressure, and precipitation, that predicts wind farm performance. Vestas previously sifted through only portions of all available data because the analysis could take several weeks per location. The company wanted to analyze more data in less time, allowing it to speed up placement calculations and increase their accuracy.

Today, Vestas does just that by using IBM InfoSphere BigInsights in its Wind and Site Competence Center. The company plans to apply the technology to hundreds of weather-related variables to calculate optimal turbine locations around the globe.

To start, Vestas will store and process an initial 2.6 PB of information comprising public weather service data and the company’s own weather data records. The data includes previously calculated information plus barometric pressure, wind direction, and other data points collected from ground level up to 300 feet in the air.

Before long Vestas will add deforestation data, satellite images, historical data, geo-spatial data, and moon and tide phase information to its analyses. The company expects to analyze even more diverse and bigger data sets reaching 20-plus petabytes over the next four years. Vestas also plans to create continuously available models of the growing data store and add new modeling techniques. The company gets better placement for its wind turbines—and we all get cleaner energy.

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