Articles | Volume 21, issue 2
https://doi.org/10.5194/nhess-21-607-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-21-607-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 Feb 2021
Research article |
| 11 Feb 2021
| 11 Feb 2021
Predicting power outages caused by extratropical storms
Finnish Meteorological Institute, B.O. 503, 00101 Helsinki, Finland
Ilona Láng
Finnish Meteorological Institute, B.O. 503, 00101 Helsinki, Finland
Alexander Jung
Aalto University, Department of Computer Science, B.O. 11000, 00076 Aalto, Finland
Antti Mäkelä
Finnish Meteorological Institute, B.O. 503, 00101 Helsinki, Finland
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Cited
17 citations as recorded by crossref.
- Assessing grid hardening strategies to improve power system performance during storms using a hybrid mechanistic-machine learning outage prediction model W. Hughes et al. 10.1016/j.ress.2024.110169
- A Probabilistic Method for Integrating Physics-Based and Data-Driven Storm Outage Prediction Models for Power Systems W. Hughes et al. 10.1061/AJRUA6.RUENG-1171
- A Hybrid Physics-Based and Data-Driven Model for Power Distribution System Infrastructure Hardening and Outage Simulation W. Hughes et al. 10.1016/j.ress.2022.108628
- Community power outage prediction modeling for the Eastern United States W. Taylor et al. 10.1016/j.egyr.2023.10.073
- Characteristics of extratropical cyclones and precursors to windstorms in northern Europe T. Laurila et al. 10.5194/wcd-2-1111-2021
- Electricity Load Lost in the Largest Windstorms—Is the Fragility-Based Model up to the Task? J. Jasiūnas et al. 10.3390/en16155678
- Influence of the Characteristics of Weather Information in a Thunderstorm-Related Power Outage Prediction System P. Watson et al. 10.3390/forecast3030034
- Machine learning for power outage prediction during hurricanes: An extensive review K. Fatima et al. 10.1016/j.engappai.2024.108056
- A Data-Driven Decision Support Tool for Anticipating Tropical Storm Impacts to the United States Power Grid P. Watson et al. 10.1109/ACCESS.2024.3442768
- An automated technique and decision support system for lightning early warning M. Alves et al. 10.1007/s13762-024-05693-7
- Usage of Methanol Fuel Cells to Reduce Power Outages in the Etelä-Savo Region, Finland P. Hernandez et al. 10.3390/environments10060096
- Crown snow load outage risk model for overhead lines R. Otto et al. 10.1016/j.apenergy.2023.121183
- Impacts of Medicanes on Geomorphology and Infrastructure in the Eastern Mediterranean, the Case of Medicane Ianos and the Ionian Islands in Western Greece M. Diakakis et al. 10.3390/w15061026
- Transmission Failure Prediction Using AI and Structural Modeling Informed by Distribution Outages S. Nyame et al. 10.1109/ACCESS.2024.3523415
- Impact of wind speed and soil frost on electricity distribution system reliability J. Haakana et al. 10.1016/j.heliyon.2024.e40846
- Bayesian Optimization and Hierarchical Forecasting of Non-Weather-Related Electric Power Outages O. Owolabi & D. Sunter 10.3390/en15061958
- Impact of Meteorological Conditions on Overhead Transmission Line Outages in Lithuania E. Rimkus et al. 10.3390/atmos15111349
17 citations as recorded by crossref.
- Assessing grid hardening strategies to improve power system performance during storms using a hybrid mechanistic-machine learning outage prediction model W. Hughes et al. 10.1016/j.ress.2024.110169
- A Probabilistic Method for Integrating Physics-Based and Data-Driven Storm Outage Prediction Models for Power Systems W. Hughes et al. 10.1061/AJRUA6.RUENG-1171
- A Hybrid Physics-Based and Data-Driven Model for Power Distribution System Infrastructure Hardening and Outage Simulation W. Hughes et al. 10.1016/j.ress.2022.108628
- Community power outage prediction modeling for the Eastern United States W. Taylor et al. 10.1016/j.egyr.2023.10.073
- Characteristics of extratropical cyclones and precursors to windstorms in northern Europe T. Laurila et al. 10.5194/wcd-2-1111-2021
- Electricity Load Lost in the Largest Windstorms—Is the Fragility-Based Model up to the Task? J. Jasiūnas et al. 10.3390/en16155678
- Influence of the Characteristics of Weather Information in a Thunderstorm-Related Power Outage Prediction System P. Watson et al. 10.3390/forecast3030034
- Machine learning for power outage prediction during hurricanes: An extensive review K. Fatima et al. 10.1016/j.engappai.2024.108056
- A Data-Driven Decision Support Tool for Anticipating Tropical Storm Impacts to the United States Power Grid P. Watson et al. 10.1109/ACCESS.2024.3442768
- An automated technique and decision support system for lightning early warning M. Alves et al. 10.1007/s13762-024-05693-7
- Usage of Methanol Fuel Cells to Reduce Power Outages in the Etelä-Savo Region, Finland P. Hernandez et al. 10.3390/environments10060096
- Crown snow load outage risk model for overhead lines R. Otto et al. 10.1016/j.apenergy.2023.121183
- Impacts of Medicanes on Geomorphology and Infrastructure in the Eastern Mediterranean, the Case of Medicane Ianos and the Ionian Islands in Western Greece M. Diakakis et al. 10.3390/w15061026
- Transmission Failure Prediction Using AI and Structural Modeling Informed by Distribution Outages S. Nyame et al. 10.1109/ACCESS.2024.3523415
- Impact of wind speed and soil frost on electricity distribution system reliability J. Haakana et al. 10.1016/j.heliyon.2024.e40846
- Bayesian Optimization and Hierarchical Forecasting of Non-Weather-Related Electric Power Outages O. Owolabi & D. Sunter 10.3390/en15061958
- Impact of Meteorological Conditions on Overhead Transmission Line Outages in Lithuania E. Rimkus et al. 10.3390/atmos15111349
Latest update: 16 Apr 2025
Short summary
Predicting the number of power outages caused by extratropical storms is a key challenge for power grid operators. We introduce a novel method to predict the storm severity for the power grid employing ERA5 reanalysis data combined with a forest inventory. The storms are first identified from the data and then classified using several machine-learning methods. While there is plenty of room to improve, the results are already usable, with support vector classifier providing the best performance.
Predicting the number of power outages caused by extratropical storms is a key challenge for...
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