We don't often think of it, but the wind represents the heating and cooling cycle of 5.5 quadrillion tons of air by a medium-sized star about eight light-minutes away. Heated air rises, and cooler air meanders, rushes, or otherwise moves in to fill the void.
While our atmosphere is fluid and dynamic, the shape of the land and the water over which the air moves is decidedly less so, which eventually creates patterns of wind and weather that can be predictable ... if you have enough data.
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Knowing the wind patterns is more than being thorough about the weather. It's also critical to optimizing wind energy production, which is growing in global capacity by double-digit percentages each year.
According to the , by the end of 2010, the power of the wind provided 430 terawatt-hours of electricity to the nations of the world, more than enough to completely power the United Kingdom, the sixth largest economy in the world.
While that sounds like a lot of power, it should be noted that, according to the same report, this also represents just 2.5 percent of global electricity demand. But statistics from show that the global capacity it tracks has grown an average of 29.2 percent annually since 1995. In five years, that could put wind power capacity at 1,548 terawatt-hours, or nine percent of current global demand.
Projections aside, there's a lot of energy to be had right now in wind energy, but like anything that depends on nature as a resource, there's also a lot of risk.
Hitting the target
If you look at the US Department of Energy's , even the most casual observer can see that some areas of the country are better suited for wind energy production than others.
And to a large degree, many onshore wind farms are indeed located in the areas of larger wind potential. If you travel to western Indiana, you should expect to find a number of wind farms, and sure enough, there are many wind turbines dotting the flat western Indiana farmland.
But getting wind turbines to be hyper-efficient means more than just plunking a few down in a generally windy area, and raking in the power and the money. Companies, investors, and power consumers must know what to expect to the highest degree of certainty. Having a turbine under-perform can drastically reduce the return on investment in these multi-million dollar machines. The opposite is true, as well. Put a wind turbine in a windier area for which it was designed, and you will damage a turbine faster, sometimes catastrophically.
This is the challenge that faces the Danish turbine manufacturer . The company has made and installed more than 43,000 land-based wind turbines in 66 nations since its inception in 1979. Vestas turbines are responsible for generating 90 terawatt-hours -- just over 20 percent -- of the world's wind power alone.
To help them achieve optimal wind turbine placement and better operational control and forecasting of the turbines once they are installed, Vestas has relied on its own wind library, which includes data from 35,000 global weather stations, as well as data that's incoming from its own turbines.
"That gives us a picture of the global flow scenario," explained Lars Christensen, the Vestas VP responsible for turbine placement and monitoring. "Those models are then cobbled to smaller models for the regional level called mesoscale models. The mesoscale models are used to establish our own huge wind library so we can pinpoint a specific location at a specific time of day and tell what the weather was like."
How detailed is the library? In its early versions, the library could give details for a grid with 27-by-27 kilometer sides (about 282 square miles). But with improvements in computational flow models, Vestas could massively increase the resolution to 10-by-10 meters-just over 1,075 feet, which is about the footprint of an average American home.
With that kind of modeling detail, the Vestas engineers needed to improve the model even further, and the best way to do that was to add data. And that's what Christensen's team did: they planned to increase the wind library tenfold with more weather data over a longer period of time.
So now the IT challenge was significant, because Vestas not only had to accept and store all of the incoming data, it had to be able to analyze that data and all of the historical facts in a timely manner. Initially, that was hard to do. Vestas would have to wait up to three weeks whenever they ran a potential site report.
"In our development strategy, we see growing our library in the range of 18 to 24 petabytes of data," Christensen said. "And while it's fairly easy to build that library, we needed to make sure that we could gain knowledge from that data."
Big data for big wind
Ultimately, Vestas decided to turn to IBM for help. Big Blue's software, IBM's Hadoop-based big data solution, turned out to be exactly what Vestas needed.
The new big data framework enabled a serious increase of data storage right off the bat. Vestas was able to increase the data resolution of those 27-square kilometer grids down to three square kilometers, without modeling. According to Christensen, this tightening of resolution drops about a month off the up-front pre-site development timetable.
Those three-week forecast analysis reports are a thing of the past, too.
"Before, it could take us three weeks to get a response to some of our questions simply because we had to process a lot of data," Christensen said. "We expect that we can get answers for the same questions now in 15 minutes."
With the time to build forecasts reduced by 97 percent, Vestas gained a significant edge over competitors, because they could get the jump on forecasting an area when talking to potential customers. Plus, since the reports were based on more accurate data models, the level of returns on turbines increased, even as the level of initial investment decreased.
Like many big data deployments, this wasn't simply a case of Vestas saying "we need a big data solution, regardless of cost." The conclusion that big data would be able to help came only after Vesta's sales and research teams started collaborating together to work on the age-old problem of increasing revenue. It quickly became apparent that by fine-tuning their data, Vestas would have a very good chance to improve. Looking at the potential gains, versus the real costs of deploying IBM's Hadoop solution, Vestas decided the expenditure would be worth it. Their decision has paid off.
And, with the increasing production of wind-generated energy, companies like Vestas can use all the savings they can get.
Vestas, a Danish wind turbine-maker, uses InfoSphere BigInsights, IBM's Hadoop-based big data solution, to build its database, drop query times, and increase revenue.
• Vestas gathers wind data from 79,000 sources, including 44,000 wind turbines
• Data queries take 15 minutes versus up to three weeks
• Vestas' wind turbines generate 20 percent of the world's wind power
• Increased data resolution from 27-square kilometer grids down to three-square kilometers
• 18-24 petabytes is the potential data available in Vestas' wind data library
This article, "," was originally published at . For the latest , analysis and how-tos, follow ITworld on and .
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