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AI and Machine Learning in the Wind Power Industry

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If you ended up here, it’s probably because you are HUGE FANS of wind turbines.

Eh eh eh … my humor sucks.

Okay, techy guys! In today’s article, we’re gonna focus on the contribution of AI and machine learning (ML) to the wind power industry.

Specifically, you’ll find out how algorithms can help us:

  • increase the accuracy of weather forecasts to predict the performance of wind farms (yep, no wind = no energy)
  • support plant monitoring and maintenance tasks
  • make electricity grids smarter

Wind energy nowadays

In an effort to avoid a toxic Blade Runner-style environment, countries are seeking to diversify their energy sources and rely more on cleaner ones.

Speaking of wind, this source can be exploited through turbines, which are positioned onshore (on land) or offshore (at sea).

Today, the majority of wind farms are built offshore to enjoy stronger and more stable wind conditions.

Besides, larger units can be transported and deployed easily, also causing less visual disturbances and potential conflicts of interest.

 

What about wind energy issues?

Is everything so cool? Well, not exactly.

Firstly, the maintenance costs to ensure optimum performance over their 20-25 year lifespan are quite high, especially in offshore locations. They can represent up to 25% of the offshore installation.

Secondly, wind energy production is very unstable, due to a wide range of external factors, starting with weather conditions.

On windless days, grid operators have to resort to conventional power plants to meet demand, buying energy on the spot market to supply the grid with prices above the average price.

Energy producers can also be penalized with fines from governments for power outages.

OUCH!

 

Too much wind energy can be even worse

On the other hand, when weather conditions allow 90% of the daily needs to be covered by wind farms, energy operators must rapidly reduce the supply from gas and coal plants to avoid overloading the entire network.

In addition to the risk of overload, producers are not compensated for the extra energy in case of exceeding the expected supply thresholds. And no one likes to work for free.

The economic weight of this variability is far from negligible. In Germany alone, the costs of adapting wind energy inputs to the power grid amount to approximately $ 550 million annually.

“In Germany alone, the costs of adapting wind energy inputs to the power grid amount to approximately $ 550 million annually.”

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