As with every passing day, we are getting close to that day, when all nonrenewable energy sources will get completely exhausted. The only solution we now have is to use renewable energy sources in a wider way.
But the problem with these sources of energy is that they are less feasible as compare to nonrenewable energy sources. Also they are more expensive, and many of them are region based.
Alphabet’s Britain-based subsidiary DeepMind has come up with its AI-based solution to strengthen the renewable energy sources and to generated a greater amount of output from them on a constant basis.
DeepMind has developed a machine learning system to predict wind power output from the farms. The AI system is capable of predicting the output 36 hours ahead of the actual generation.
The AI has been trained on weather forecasts and historical turbine data and it makes recommendations on “how to make optimal hourly delivery commitments to power grid a full day in advance.”
“Based on these predictions, our model recommends how to make optimal hourly delivery commitments to the power grid a full day in advance,” Witherspoon and Fadrhonc said.
According to Google, the AI system has improved the value of the wind energy, these farms are providing by 20 percent over a baseline where no such time-based predictions are being performed.
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.”
DeepMind says with this ML system they can now schedule set deliveries of energy output, which are more valuable to the grid than standard, non-time-based deliveries
“Over the past decade, wind farms have become an important source of carbon-free electricity as the cost of turbines has plummeted and adoption has surged,” Sims Witherspoon, Programme Manager at DeepMind and Will Fadrhonc, Carbon Free Energy Programme Lead at Google wrote in a blog post this week.
Google says “the variable nature of wind itself makes it an unpredictable energy source — less useful than one that can reliably deliver power at a set time,” due to having to rely on nature to generate the needed electricity demands of the grid
“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,” write Sims Witherspoon, a product manager at DeepMind, and Will Fadrhonc, Google’s Carbon Free Energy program lead, in a co-authored blog post.
“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.”
Google says “the variable nature of wind itself makes it an unpredictable energy source — less useful than one that can reliably deliver power at a set time,” due to having to rely on nature to generate the needed electricity demands of the grid.
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