Renewable energy decision makers are required to make critical judgments on a daily basis with regard to energy generation, distribution, demand, storage, and integration. Accurate knowledge of the present and future state of the atmosphere is vital in making these decisions.
Wind and solar energy are among the most difficult weather variables to forecast. Topography, surface roughness, ground cover, temperature inversions, foliage, gravity waves, low–level jets, clouds, and aerosols, all affect wind and solar energy prediction skill.
The unpredictability of renewable energy sources like wind and solar creates reliability challenges for utilities seeking to balance power supply and demand across centralized grid networks. Policies compelling energy providers to incorporate more renewable energy into their portfolios make the challenge more urgent, but also create business opportunities for companies finding solutions.
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.
The new system is as much as 30% more accurate than today’s state-of-the-art weather prediction systems used by organizations such as the National Weather Service, according to the National Renewable Energy Laboratory.
IBM’s SMT system uses weather patterns gleaned from thousands of data points and predict how much solar and wind power will be available weeks in advance. (Credit: IBM)
“It’s providing a forecast for solar, wind and other environmental parameters. It learns from solar plants [and] weather stations, and constantly adjusts and improves the forecast,” said Hendrik Hamann, Research Manager at the IBM T.J. Watson Research Center.
Because the system can better predict how much renewable energy will be available, the nation’s power grids are better able to integrate that electricity with traditional forms of power.
IBM Improving Solar Forecasting Technology (Video credit: IBM Research)
IBM’s new computer algorithm, called Self-Learning Weather Model and Renewable Forecasting Technology (thankfully known as SMT), uses big data analytics and machine learning to improve solar forecasts. The system works by combining more than 1TB of data gleaned daily through more than 1,600 weather-monitoring stations and solar and wind plants in the continental U.S., as well as from weather satellites.
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