Artificial intelligence (AI) is being used to predict everything from crime to psychotic episodes. Now, blackouts can be added to the list.

More specifically, the tool is being used to predict blackouts that originate from thunderstorms. This is especially useful for electricity companies who want to be able to predict damage to their infrastructure.

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In light of this, Roope Tervo, a software architect at the Finnish Meteorological Institute (FMI) and PhD researcher at Aalto university in Professor Alex Jung's research group, has conceived of a machine learning approach to predict how severe storms may be.

To achieve this Tervo first fed the system data from power-outages. That data was provided by three Finnish energy companies, Järvi-Suomen Energia, Loiste Sähkoverkko, and Imatra Seudun Sähkönsiirto.

"Storms were sorted into 4 classes. A class 0 storm didn't knock out electricity to any power transformers. A class 1 storm cut-off up to 10% of transformers, a class 2 up to 50%, and a class 3 storm cut power to over 50% of the transformers," revealed an Aalto University statement.

Data that is easy to understand

Secondly, Tervo took the data from the storms and made it easy for the computer to understand.

"We used a new object-based approach to preparing the data, which was makes this work exciting" said Tervo. "Storms are made up of many elements that can indicate how damaging they can be: surface area, wind speed, temperature and pressure, to name a few. By grouping 16 different features of each storm, we were able to train the computer to recognize when storms will be damaging."

The results proved fruitful with the system being able to easily identify between class 0 and 3 storms. Now, the researchers are including additional data for the algorithm to be able to differentiate between class 1 and 2 storms.

"Our next step is to try and refine the model so it works for more weather than just summer storms," said Tervo, "as we all know, there can be big storms in winter in Finland, but they work differently to summer storms so we need different methods to predict their potential damage"