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IBM's machine-learning crystal ball can foresee renewable energy availability

Lucas Mearian | July 20, 2015
IBM has developed a computer system that can learn about weather from thousands of data points and predict days -- even weeks -- in advance how much power from solar and wind farms will be available for the U.S. power grid.

Over the past two years, solar energy has been the second-largest source of new electricity generating capacity in the U.S., exceeded only by natural gas.

As the amount of solar and wind capacity continues to grow, it's critical for regional power grids to know how much renewable electricity they'll have in advance to better plan their capacity needs.

Power transmission in the U.S. is controlled by regional independent system operators (ISOs), which receive electricity from member utilities. For example, ISO New England Inc. manages the operation of the region's bulk electric power system and transmission lines.

Power from traditional sources, such as fossil-fuel power plants and hydroelectric dams, is relatively stable and easy to predict. Renewable power, however, is at the whim of the weather; clouds, humidity and wind can affect how much renewable energy is generated from solar and wind turbines.

IBM's SMT system takes in new data every 15 minutes, allowing it to predict and then inform ISOs how much energy is likely to be produced from solar and wind farms.

"By improving the accuracy of forecasting, utilities can operate more efficiently and profitably," said Bri-Mathias Hodge, who oversees the Transmission and Grid Integration Group at the National Renewable Energy Laboratory (NREL), a collaborator in the SMT project. "That can increase the use of renewable energy sources as a more accepted energy generation option."

 

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