Google and DeepMind Are Using Machine Learning to Predict Wind Energy Output

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Google’s partnership with DeepMind to make wind power more predictable and valuable is a concrete step toward the aspiration of sourcing carbon-free energy on a 24×7 basis.

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. However, 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.

In search of a solution to this problem, last year DeepMind and Google (both under Alphabet) started applying machine learning algorithms to 700 megawatts of wind power capacity in the central United States. These wind farms which are a part of Google’s global fleet of renewable energy projects, which collectively generate as much electricity as is needed to power a medium-sized city.

Using a neural network trained on widely available weather forecasts and historical turbine data, the team configured the DeepMind system to predict wind power output 36 hours ahead of actual generation. Based on these predictions, the model recommends how to make optimal hourly delivery commitments to the power grid a full day in advance. This is important because energy sources that can be scheduled (i.e. can deliver a set amount of electricity at a set time) are often more valuable to the grid.

To date, machine learning has boosted the value of our wind energy by roughly 20 percent, compared to the baseline scenario of no time-based commitments to 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. 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,” DeepMind issued in a statement.

The team is now hoping that this kind of machine learning approach can strengthen the business case for wind power and drive further adoption of carbon-free energy on electric grids worldwide.