Google's London-based AI development subsidiary DeepMind has developed and trained a machine learning system capable of forecasting the amount of power that can be generated by wind farms - up to 36 hours in advance.
According to Google, the newly developed system helped electricity grid managers schedule set 'deliveries' of power, which are more valuable to the grid than standard, non-time-based deliveries.
Due to the variable nature of wind, it is usually difficult to accurately forecast the amount of power that a wind farm is likely to produce during a specific time period.
But DeepMind claims that its neural network, which has been trained on historical turbine data and weather patterns, can forecast the level of power output by its wind turbines at its Oklahoma wind farm in the US, 36 hours in advance, with a reasonable amount of accuracy.
The new AI model was applied to 700 megawatts of energy produced by more than 90 turbines in Oklahoma in central US. The model accurately recommended the best way to make hourly delivery commitments to the power grid a full day in advance, thus increasing the value of wind energy by about 20 per cent.
According to Google, the system enables the wind farm's managers to schedule when to supply a specific amount of electricity to the power grid and to have a better clue of pricing models.
The system is also helpful for engineers in scheduling maintenance for turbines.
Google has recently collaborated with several wind energy firms across the world in an effort to operate completely on green energy. The goal, which was first proposed in 2016, was eventually achieved in 2017.
The company also hopes that the machine learning approach that has been implemented in wind turbine farm in Oklahoma would drive further adoption of green energy on electric grids around the world.
"Researchers and practitioners across the energy industry are developing novel ideas for how society can make the most of variable power sources like solar and wind," Google researchers wrote in a blog post.
"We're eager to join them in exploring general availability of these cloud-based machine learning strategies," they added.
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