Tech giants Google and its A.I. subsidiary DeepMind have been working on a way to increase the usefulness of green energy produced by wind farms.
The problem the company has been trying to solve is that, while wind energy represents an important source of carbon-free electricity, it is fundamentally unpredictable.
As a result, despite its positive points, wind power is less useful to the power grid than power sources that can reliably deliver it at set times.
By using machine learning artificial intelligence to predict wind output, Google and DeepMind have trained a neural network to accurately predict wind power output 36 hours ahead of the power being generated.
Using these predictions, a computer model can then make recommendations for “optimal hourly delivery commitments” to the grid a whole day in advance.
This makes it far more valuable since it means that it can be relied upon to deliver a set amount of electricity at a particular time.
“We can’t eliminate the variability of the wind, but our early results suggest that we can use machine learning to make wind power sufficiently more predictable and valuable,” Google and DeepMind researchers note in a blog post.
They added, “This approach also helps bring greater data rigor to wind farm operations, as machine learning can help wind farm operators make smarter, faster, and more data-driven assessments of how their power output can meet electricity demand.”
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