Intense thunderstorms are common during summer and many of them cause power outages or ‘blackouts’. To tackle the issue, Roope Tervo, a software architect at the Finnish Meteorological Institute (FMI) and PhD researcher at Aalto University in Professor Alex Jung’s research group, have created an ML-driven concept to predict the potential impact or severity of storms.
Using data from past power outages, the autonomous machine can categorise storms into levels of severity, helping experts make accurate predictions about their potential impact and plan accordingly.
Speaking to Science Daily about the innovation, Roope Tervo, explained:
“We used a new object-based approach to preparing the data, which made this work exciting.
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.”
While this machine learning storm prediction technology is still in development, if successful, it could save a great deal of time and investment in blackout or power outage management in the not so distant future.
To discover more about the inner workings of this ML innovation, read the full report at Science Daily.