Sims Witherspoon: Wind energy can be unpredictable. AI could help Sims Witherspoon is a researcher using AI to fight climate change. She says AI can help solve the biggest problem with renewables like wind and solar: their unpredictable nature.

Wind energy can be unpredictable. AI can help

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MANOUSH ZOMORODI, HOST:

We have talked a lot about the downsides of technology on this episode, but we want to end it on a positive note with a specific story about how artificial intelligence is being developed to tackle climate change. At the AI research lab DeepMind, Sims Witherspoon, and her team are training AI to speed up the transition to renewables like wind. She explained the process at the TED Countdown climate conference in 2023.

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SIMS WITHERSPOON: Today, I'd like to talk about how we can use AI to harness a superpower we already have in this fight - wind energy. Renewables are unquestionably a key to a sustainable future, but the problem is, they're unpredictable. Sometimes, the sun shines and the wind blows, and sometimes, it just doesn't. For an electricity systems operator who needs supply to meet demand in real-time 24/7, this is hugely problematic. Renewables can't be 100% reliably scheduled.

Now, unfortunately, fossil fuel plants are the opposite. You can burn a specific amount of coal at a set time to deliver exactly the amount of electricity you want in a predictable time window. If you're a power systems manager whose job is to literally keep the lights on, which source are you more confident depending on?

But here's one of the places where AI can come in. It is a powerful tool for forecasting. AI systems can ingest vast amounts of historical data and help us predict future events. While we can't eliminate the variability of wind, we can use AI to more accurately predict its availability. That was my team's what to do - use AI to accelerate the transition to renewables like wind energy. The tough part was the how to do it.

Our team, which is a mix of research scientists, engineers, product manager, program manager and an impact analyst, decided that a neural net trained on historical weather data and turbine power production information would likely help us accomplish our goal.

There are massive gaps in climate-critical data not just in electricity but in agriculture, transportation, industry and many other sectors. Some of our data we could purchase or download for free - weather forecasts, for instance. But some of the data we needed was proprietary. This would be like turbine power production information and other operational data from the wind farms. We needed that proprietary data so that we could train our models to learn the relationship between historical weather and historical power production so it could then make predictions about future power availability based on what data said about future weather.

In addition to data, in order to prove that AI works, we have to have deployment opportunities in the real world. Luckily, for us, Google was a ready and willing partner. They let us test on 700 megawatts of their wind power capacity, which is equivalent to a large wind farm in the United States. This made them an excellent proxy for external wind farm operators. They also lent us an expert team to advise on metrics and benchmarks and to share the data that we needed. So at this point, we have our idea; we have our data; we have our deployment partner. Now to test and deploy our system.

Improving the accuracy of electricity supply forecast is incredibly important. If predictions are higher than actual generation, renewable electricity managers may not have enough supply to meet demand. Now, this, in turn, drives the purchase of carbon-intensive fossil fuels to cover that gap because they're largely what makes up backup generation.

Now, the good news? Our AI system performed 20% better than Google's existing systems. Even better news is that Google decided to scale this technology, and scaling is so important. We will run out of time in the climate countdown if we aren't deploying solutions that are widely applicable. This particular solution is being developed into a software product that French company Engie is among the first to pilot. But it doesn't even take a major research organization to do this kind of work. Where we focused on AI for supply-side forecasting, a small U.K.-based nonprofit called Open Climate Fix is focusing on AI for demand-side forecasting.

They found a willing partner in the U.K. National Grid and are currently deploying forecasts that are two times more accurate than the U.K. Grid's previously used systems. Now, all of this is to say that AI can help us with the transition to renewable energy. But scientists and technologists, we're not going to be able to do that alone. We need to be working with partners and experts who can teach us the how.

Now for the warning label. AI is not a silver bullet. It will not solve all problems driving climate change. It isn't even the right tool for many of the challenges that we face. AI is also not a technology without tensions. It needs to be deployed safely and responsibly. Not to mention, until our grids are run on clean energy, AI itself will carry a carbon footprint, as will any energy-intensive technology we use. But AI can be a transformational tool in our fight against climate change. It's just on all of us to wield it effectively. Thank you.

(APPLAUSE)

ZOMORODI: That was DeepMind's AI developer, Sims Witherspoon. You can see her full talk at ted.com. Many thanks for listening to our show on Tech's Climate Conundrum. This episode was produced by James Delahoussaye, Matthew Cloutier, Harsha Nahata and Rachel Faulkner White. It was edited by Sanaz Meshkinpour and me. Our production staff at NPR also includes Katie Monteleone and Fiona Geiran. Irene Noguchi is our executive producer. Our audio engineer was Gilly Moon. Our theme music was written by Ramtin Arablouei. Our partners at TED are Chris Anderson, Helen Walters, Alejandra Salazar and Daniella Balarezo. I'm Manoush Zomorodi, and you've been listening to the TED Radio Hour from NPR.

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