Using machine learning methods to forecast the number of power outages at substations
Forecasting in the energy sector is of great importance for suppliers and for consumers. Optimum power consumption depends on many factors. Due to natural or any other external conditions, accidents are possible. In order to minimize emergency consequences, it is necessary to be prepared for possibl...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2023-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/27/e3sconf_agritechviii2023_06034.pdf |
| _version_ | 1797807875527016448 |
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| author | Minnegalieva Chulpan Gainullina Alina |
| author_facet | Minnegalieva Chulpan Gainullina Alina |
| author_sort | Minnegalieva Chulpan |
| collection | DOAJ |
| description | Forecasting in the energy sector is of great importance for suppliers and for consumers. Optimum power consumption depends on many factors. Due to natural or any other external conditions, accidents are possible. In order to minimize emergency consequences, it is necessary to be prepared for possible outages in advance in order to reduce the time for their elimination and decision-making. This article considers the problem of forecasting power outages at substations. The enterprise provided a summary table of outages at substations due to natural disasters on specific days. To solve the problem, a machine learning method was chosen – binary classification. Five different algorithms were considered. The models were tested on data from the first half of 2022. The most effective algorithm for 20% of the test sample was the binary classification algorithm using generalized additive models (GAM). This algorithm is also one of the best with a sample of 50%. A model has been prepared for further use in predicting the probability of outages at the enterprise. The model can be used in other organizations; for this, it is first necessary to train the model on the data of the corresponding region. |
| first_indexed | 2024-03-13T06:29:09Z |
| format | Article |
| id | doaj.art-b8451605c0a04b678968738d066701f7 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-13T06:29:09Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-b8451605c0a04b678968738d066701f72023-06-09T09:11:21ZengEDP SciencesE3S Web of Conferences2267-12422023-01-013900603410.1051/e3sconf/202339006034e3sconf_agritechviii2023_06034Using machine learning methods to forecast the number of power outages at substationsMinnegalieva Chulpan0Gainullina Alina1Institute of Computational Mathematics and Information Technologies, Kazan Federal UniversityInstitute of Computational Mathematics and Information Technologies, Kazan Federal UniversityForecasting in the energy sector is of great importance for suppliers and for consumers. Optimum power consumption depends on many factors. Due to natural or any other external conditions, accidents are possible. In order to minimize emergency consequences, it is necessary to be prepared for possible outages in advance in order to reduce the time for their elimination and decision-making. This article considers the problem of forecasting power outages at substations. The enterprise provided a summary table of outages at substations due to natural disasters on specific days. To solve the problem, a machine learning method was chosen – binary classification. Five different algorithms were considered. The models were tested on data from the first half of 2022. The most effective algorithm for 20% of the test sample was the binary classification algorithm using generalized additive models (GAM). This algorithm is also one of the best with a sample of 50%. A model has been prepared for further use in predicting the probability of outages at the enterprise. The model can be used in other organizations; for this, it is first necessary to train the model on the data of the corresponding region.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/27/e3sconf_agritechviii2023_06034.pdf |
| spellingShingle | Minnegalieva Chulpan Gainullina Alina Using machine learning methods to forecast the number of power outages at substations E3S Web of Conferences |
| title | Using machine learning methods to forecast the number of power outages at substations |
| title_full | Using machine learning methods to forecast the number of power outages at substations |
| title_fullStr | Using machine learning methods to forecast the number of power outages at substations |
| title_full_unstemmed | Using machine learning methods to forecast the number of power outages at substations |
| title_short | Using machine learning methods to forecast the number of power outages at substations |
| title_sort | using machine learning methods to forecast the number of power outages at substations |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/27/e3sconf_agritechviii2023_06034.pdf |
| work_keys_str_mv | AT minnegalievachulpan usingmachinelearningmethodstoforecastthenumberofpoweroutagesatsubstations AT gainullinaalina usingmachinelearningmethodstoforecastthenumberofpoweroutagesatsubstations |