Alphabet's DeepMind, an artificial intelligence (AI) firm, has used machine learning to boost the productivity of wind energy.
In a blogpost Tuesday, DeepMind's Carl Elkin and Sims Witherspoon, together with Google's Will Fadrhonc, described how in 2018 DeepMind and Google had started to apply "machine learning algorithms to 700 megawatts of wind power capacity in the central United States."
The post explained how a neural network was trained on weather forecasts and historical turbine data. The DeepMind system was configured in order to "predict wind power output 36 hours ahead of actual generation."
This essentially means that the technology deployed by DeepMind can predict how much energy wind turbines and farms can produce.
The model could then make recommendations on "how to make optimal hourly delivery commitments to the power grid a full day in advance."
DeepMind described this as important, noting that sources of energy that can "deliver a set amount of electricity at a set time" were often more valuable to the grid.
In the renewable energy sector, tools such as this could become crucial, given that sources like the sun and wind do not promise a constant stream of power.
Using machine learning at the wind farms had generated positive results, DeepMind said, adding that it had "boosted the value of our wind energy by roughly 20 percent, compared to the baseline scenario of no time-based commitments to the grid."
Based in London, DeepMind was acquired by Google in 2014. Today, it's part of Alphabet, which is the parent company of Google.
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