Machine learning is making wind power more predictable.

What’s new: Engie SA, a multinational energy utility based in France, is the first customer for an AI-powered tool from Google that predicts the energy output of wind farms, Bloomberg reported. The company plans to deploy the system on 13 wind farms in Germany.

How it works: Google’s DeepMind subsidiary trained a neural network to predict energy output from wind farms up to 36 hours ahead of time. The training data included historical weather forecasts and unspecified data from wind turbines.

  • Engie will use the system to predict how much energy will be available to sell to electricity providers in coming days.
  • Accurate predictions should also enable Engie to reduce its use of fossil fuels and nuclear power. If the system predicts low wind-power output, the company can plan to bring other energy sources online.
  • In a 2019 blog post, Google reported that the increased reliability afforded by its algorithm would add 20 percent to the value of wind energy.

Behind the news: Google isn’t the only firm employing machine learning to squeeze more electricity out of renewable resources.

  • Microsoft recently partnered with Danish wind turbine manufacturer Vesta Wind Systems to develop a reinforcement-learning system that helps keep turbines pointed in the optimal direction.
  • Israeli startup Xfloat built a system that keeps floating solar panels facing the sun as it moves across the sky.

Why it matters: Wind and solar power are notoriously uncertain, leading utilities to default to fossil fuels, which are available on-demand. Predicting wind-energy yields can reduce some of that uncertainty, helping utilities benefit from advantages such as renewables’ lower overhead and easing dependence on fossil-fuel and nuclear sources.

We’re thinking: Stopping climate change isn’t the only motivation to cut dependence on fossil fuels. The conflict in Ukraine has contributed to a global shortage of oil and gas, causing energy prices to spike. Alternative sources can help make the global economy less reliant on oil producers and more resilient to disruptions in supply.


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