How Machine Learning Could Impact the Future of Renewable Energy

More and more cities are looking to go green. And renewable energy is, if current trends hold, the future of the energy industry.

But as renewable energy technologies like wind farms are implemented at larger scales than ever, local officials are running into their limitations. The energy production of wind farms is hard to predict, and this makes energy grid design difficult.

Experts hope that machine learning can be applied to renewable energy to solve this problem. If it works, this new tech may make energy officials more enthusiastic about implementing renewables.

One downside of renewables is how hard it can be to predict the energy they produce. Wind speeds can vary widely from hour to hour and from day to day. You can average out how much wind a certain place gets over the course of a long period of time. And you can also use that information to figure out how much energy a wind farm may produce per year. But it’s much harder to accurately predict the energy a wind farm will produce on a given day or at a certain time.

Inaccurate predictions mean it’s harder to know if construction costs will be worth it. With renewables, too much and too little are both big problems. Create too little power and you’ll need to have supplemental energy sources at the ready. Generate too much power and you’ll need to either store that energy or waste it. And battery technology is just too expensive right now to store renewable energy at any sort of useful scale.

Machine learning technology — computer programs that use data sets to “learn” how to see patterns in information like wind speed and energy output — may be the answer to wind farms’ prediction problem.

The same machine learning tech, experts think, could be used to make green energy more predictable. In February 2019, Google announced that it was using DeepMind, the company’s in-house machine learning technology, to predict the energy output of wind farms.

The machine learning technology has already made wind farm predictions 20 percent more valuable, according to Google. And better value means that wind farms may be seen as a safer investment by municipal officials who control which kinds of energy projects get built.

Will machine learning build better wind farms? It’s hard to say. But machine learning has been successful in related fields.

The weather is notoriously difficult to predict, for many of the same reasons that it’s hard to predict wind speeds. A good prediction needs to take into account more variables than a person can keep track of — like changing levels of humidity, pressure and temperature. Predicting the weather is so hard, in fact, that IBM acquired The Weather Company to see if machine learning could make weather predictions better. The results? According to IBM, they achieved a nearly 200 percent increase in the accuracy of forecasts. […]

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