Carbon-free technologies like renewable energy help combat climate change, but many of them have not reached their full potential. Consider wind power: 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 started applying machine learning algorithms to 700 megawatts of wind power capacity in the central United States. These wind farms--part of Google's global fleet of renewable energy projects--collectively generate as much electricity as is needed by a medium-sized city.
In collaboration with its Britain-based Artificial Intelligence (AI) subsidiary DeepMind, Google has developed a system to predict wind power output 36 hours ahead of actual generation. Google said that these type of predictions can boost the value of wind energy and can strengthen the business case for wind power and drive further adoption of carbon-free energy on electric grids worldwide. "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. "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," they said. In search of a solution to this problem, DeepMind and Google started applying machine learning algorithms to 700 megawatts of wind power capacity in the central US.
Google may have dropped the motto "don't be evil" from its corporate code of conduct, but it seems that the search giant still wants to use its superpowers for good. With that mission in mind, 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.
Wind farms have traditionally made less money for the electricity they produce because they have been unable to predict how windy it will be tomorrow. "The way a lot of power markets work is you have to schedule your assets a day ahead," said Michael Terrell, the head of energy market strategy at Google. "And you tend to get compensated higher when you do that than if you sell into the market real-time. "Well, how do variable assets like wind schedule a day ahead when you don't know the wind is going to blow?" Terrell asked, "and how can you actually reserve your place in line?" Here's how: Google and the Google-owned Artificial Intelligence firm DeepMind combined weather data with power data from 700 megawatts of wind energy that Google sources in the Central United States. Using machine learning, they have been able to better predict wind production, better predict electricity supply and demand, and as a result, reduce operating costs. "What we've been doing is working in partnership with the DeepMind team to use machine learning to take the weather data that's available publicly, actually forecast what we think the wind production will be the next day, and bid that wind into the day-ahead markets," Terrell said in a recent seminar hosted by the Stanford Precourt Institute of Energy. Stanford University posted video of the seminar last week. The result has been a 20 percent increase in revenue for wind farms, Terrell said. The Department of Energy listed improved wind forecasting as a first priority in its 2015 Wind Vision report, largely to improve reliability: "Improve Wind Resource Characterization," the report said at the top of its list of goals. "Collect data and develop models to improve wind forecasting at multiple temporal scales--e.g., minutes, hours, days, months, years." Google's goal has been more sweeping: to scrub carbon entirely from its energy portfolio, which consumes as much power as two San Franciscos. Google achieved an initial milestone by matching its annual energy use with its annual renewable-energy procurement, Terrell said. But the company has not been carbon-free in every location at every hour, which is now its new goal--what Terrell calls its "24x7 carbon-free" goal. "We're really starting to turn our efforts in this direction, and we're finding that it's not something that's easy to do.
Google's DeepMind is using machine learning to predict the performance of its wind turbines 36 hours in advance. The prediction of wind turbine performance for turbines in the central United States more than a day in advance has led to a roughly 20 percent increase in the value of wind energy, Google and DeepMind said in a joint blog post today. The model is trained using weather data and historical wind turbine performance data. "Based on these predictions, our 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," the post reads.