A large synthetic dataset for machine learning applications in power transmission grids
Abstract With the ongoing energy transition, power grids are evolving fast. They operate more and more often close to their technical limit, under more and more volatile conditions. Fast, essentially real-time computational approaches to evaluate their operational safety, stability and reliability a...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-04479-x |
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| author | Marc Gillioz Guillaume Dubuis Philippe Jacquod |
| author_facet | Marc Gillioz Guillaume Dubuis Philippe Jacquod |
| author_sort | Marc Gillioz |
| collection | DOAJ |
| description | Abstract With the ongoing energy transition, power grids are evolving fast. They operate more and more often close to their technical limit, under more and more volatile conditions. Fast, essentially real-time computational approaches to evaluate their operational safety, stability and reliability are therefore highly desirable. Machine Learning methods have been advocated to solve this challenge, however they are heavy consumers of training and testing data, while historical operational data for real-world power grids are hard if not impossible to access. This manuscript presents a large synthetic dataset of power injections in an electric transmission grid model of continental Europe, and describes the algorithm developed for its generation. The method allows one to generate arbitrarily large time series from the knowledge of the grid – the admittance of its lines as well as the location, type and capacity of its power generators – and aggregated power consumption data, such as the national load data given by ENTSO-E. The obtained datasets are statistically validated against real-world data. |
| format | Article |
| id | doaj-art-ed3486f832ca4a1c9149528681c4b36e |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-ed3486f832ca4a1c9149528681c4b36e2025-02-02T12:08:22ZengNature PortfolioScientific Data2052-44632025-01-0112111410.1038/s41597-025-04479-xA large synthetic dataset for machine learning applications in power transmission gridsMarc Gillioz0Guillaume Dubuis1Philippe Jacquod2School of Engineering, University of Applied Sciences and Arts of Western Switzerland HES-SOSchool of Engineering, University of Applied Sciences and Arts of Western Switzerland HES-SOSchool of Engineering, University of Applied Sciences and Arts of Western Switzerland HES-SOAbstract With the ongoing energy transition, power grids are evolving fast. They operate more and more often close to their technical limit, under more and more volatile conditions. Fast, essentially real-time computational approaches to evaluate their operational safety, stability and reliability are therefore highly desirable. Machine Learning methods have been advocated to solve this challenge, however they are heavy consumers of training and testing data, while historical operational data for real-world power grids are hard if not impossible to access. This manuscript presents a large synthetic dataset of power injections in an electric transmission grid model of continental Europe, and describes the algorithm developed for its generation. The method allows one to generate arbitrarily large time series from the knowledge of the grid – the admittance of its lines as well as the location, type and capacity of its power generators – and aggregated power consumption data, such as the national load data given by ENTSO-E. The obtained datasets are statistically validated against real-world data.https://doi.org/10.1038/s41597-025-04479-x |
| spellingShingle | Marc Gillioz Guillaume Dubuis Philippe Jacquod A large synthetic dataset for machine learning applications in power transmission grids Scientific Data |
| title | A large synthetic dataset for machine learning applications in power transmission grids |
| title_full | A large synthetic dataset for machine learning applications in power transmission grids |
| title_fullStr | A large synthetic dataset for machine learning applications in power transmission grids |
| title_full_unstemmed | A large synthetic dataset for machine learning applications in power transmission grids |
| title_short | A large synthetic dataset for machine learning applications in power transmission grids |
| title_sort | large synthetic dataset for machine learning applications in power transmission grids |
| url | https://doi.org/10.1038/s41597-025-04479-x |
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