Machine learning helps to predict blackouts caused by storms
Thunderstorms are common all over the world in summer. As well as spoiling afternoons in the park, lightning, rain and strong winds can damage power grids and cause electricity blackouts. It鈥檚 easy to tell when a storm is coming, but electricity companies want to be able to predict which ones have the potential to damage their infrastructure.
Machine learning 鈥 when computers find patterns in existing data which enable them to make predictions for new data 鈥 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鈥檚 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, J盲rvi-Suomen Energia, Loiste S盲hkoverkko, and Imatra Seudun S盲hk枚nsiirto, 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鈥檛 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.
The next step was taking the data from the storms that FMI had, and making it easy for the computer to understand. 鈥淲e used a new object-based approach to preparing the data, which was makes this work exciting鈥 said Roope. 鈥淪torms 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鈥.
The results were promising: the algorithm was very good at predicting which storms would be a class 0 and cause no damage, and which storms would be at least a class 3 and cause lots of damage. The researchers are adding more data for storms into the model to help improve the ability to tell class 1 and 2 storms apart from each other, to make the prediction tools even more useful to the energy companies.
鈥淥ur next step is to try and refine the model so it works for more weather than just summer storms,鈥 said Roope, 鈥渁s 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鈥
Paper link:
R. Tervo, J. Karjalainen and A. Jung, "Short-Term Prediction of Electricity Outages Caused by Convective Storms," in IEEE Transactions on Geoscience and Remote Sensing.
doi: 10.1109/TGRS.2019.2921809 URL:
Read more news
Funding for impact assessment research concerning the Helsinki Capital region available
Funding call of the Urban Impact project (Kaupunkivaikutus) is open until 11. October 2026.
Aalto Distinguished Professor Jukka Pekola鈥檚 message: Basic research is at the heart of progress
Department of Applied Physics Professor Jukka Pekola was appointed Aalto Distinguished Professor on September 1, 2026.
Unite! Seed Fund 2026 鈥 Aalto involved in eight projects
From the 2026 Seed Fund call, 22 proposals were selected for funding. Aalto is involved in eight of these projects and coordinates one.