Solar power was the fastest-growing form of electricity generation in the United States in 2014. As renewable energy continues to expand, demand is growing for a better way to predict just how much power from these intermittent sources will be available for the grid.
IBM shared new details last week on its program to harness powerful computers to forecast weather and other factors that determine the output of solar and wind installations. Using machine learning and advanced data analytics, IBM is making an aggressive push to give utilities, plant managers, and grid operators clearer guidance on what their arrays will put out today, tomorrow, next week, and even months from now.
At last week's European Control Conference in Linz, Austria, scientists from IBM and the National Renewable Energy Laboratory (NREL) said they will make the forecasts available, free of charge, to users across the continental United States.
Solar and wind forecasts produced by IBM's technology are as much as 30% more accurate than conventional forecasts, according to Hendrik Hamann, a research manager at IBM. Such precision could make it possible to avoid generating hundreds of megawatts of excess power every year and reduce the need for new "peaker" plants to supply power in times of peak demand, potentially lowering carbon emissions and saving utilities and ratepayers millions of dollars. An NREL study of the independent system operator for New England found that making solar forecasting 25% more accurate would offer potential cost savings of $46.5 million a year across the region.
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