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IBM: DOE Solar Forecast Accuracy Up 30% With SMT Machine Learning Tool

IBM: DOE Solar Forecast Accuracy Up 30% With SMT Machine Learning Tool - top government contractors - best government contracting event
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solar powerThe Energy Department has achieved a 30 percent increase in accuracy on wind, hydro and solar forecasts through the use of an IBM-built system that uses machine learning techniques, the company said Thursday.

IBM said the Self-learning weather Model and renewable forecasting Technology platform is designed to analyze and generate weather model-derived solar forecasts through analytics, big data and other cognitive computing systems.

“By improving the accuracy of forecasting, utilities can operate more efficiently and profitably,” said Dr. Bri-Mathias Hodge, head of the transmission and grid integration group at the National Renewable Energy Laboratory.

“That can increase the use of renewable energy sources as a more accepted energy generation option.”

The SMT system is part of DOE’s SunShot Initiative, which intends to develop platforms meant to bolster adoption of solar energy.

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Written by Jane Edwards

is a staff writer at Executive Mosaic, where she writes for ExecutiveBiz about IT modernization, cybersecurity, space procurement and industry leaders’ perspectives on government technology trends.

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