Machine learning -- when computers find patterns in existing data which enable them to make predictions for new data -- is ideal for predicting which storms might cause blackouts. 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 developed a machine learning approach to predict the severity of storms.
The first step of teaching the computer how to categorise the storms was by providing them with data from power-outages. Three Finnish energy companies, Järvi-Suomen Energia, Loiste Sähkoverkko, and Imatra Seudun Sähkönsiirto, who have power grids through storm-prone central Finland, provided data about the amount of power disruptions to their network. 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.
Step - Data - Storms - FMI - Computer
The next step was taking the data from the storms that FMI had, and making it easy for the computer to understand. "We used a new object-based...
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