Place of Neural Network Methods for Predicting Power Consumption in Railway Transport
The article shows the relevance of forecasting electricity consumption using an automated electric energy metering system that takes into account various levels of the hierarchy of the power supply system. The features of modern management of fuel and energy resources of the enterprise are considere...
| Main Authors: | , |
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| Format: | Article |
| Language: | Russian |
| Published: |
The Fund for Promotion of Internet media, IT education, human development «League Internet Media»
2022-03-01
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| Series: | Современные информационные технологии и IT-образование |
| Subjects: | |
| Online Access: | http://sitito.cs.msu.ru/index.php/SITITO/article/view/829 |
| _version_ | 1811216581619875840 |
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| author | Valeriya Osipova Dmitry Yakovlev |
| author_facet | Valeriya Osipova Dmitry Yakovlev |
| author_sort | Valeriya Osipova |
| collection | DOAJ |
| description | The article shows the relevance of forecasting electricity consumption using an automated electric energy metering system that takes into account various levels of the hierarchy of the power supply system. The features of modern management of fuel and energy resources of the enterprise are considered, using the example of the Trans-Baikal Directorate for Energy Supply ‒ a structural subdivision of Transenergo ‒ a branch of the open joint stock company "Russian Railways". The rational use of fuel and energy resources, the introduction of energy-saving and resource–saving technologies are among the most relevant both in Russia as a whole and in railway transport in particular. The specifics of Russia's economic development and the specifics of the formation of tariffs in the electric energy market have led to an increase in its cost. In this regard, reducing the cost of purchasing fuel and energy resources is one of the main goals of the energy strategy of railway transport. The result of the energy-saving policy of railway transport, in almost all components of the consumption of fuel and energy resources, excluding train traction, is a reduction in the cost of buying electric energy in the situation of reforming electric energy by entering the wholesale electric energy market (RE). It makes possible to reduce the cost of the consumed energy significantly, so the cost of electricity received by the railway from the EPR will be greatly lower than the cost of electricity received from the guaranteeing supplier ‒ the regional power system.
The paper analyzes the factors affecting on the amount of electric energy consumption for train traction. Neural network models with high approximating ability are considered, which allow processing statistical information and performing predictive estimates. It is shown that multilayer neural networks should be considered the most acceptable for predicting power consumption. The method of neural network forecasting of electric energy consumption is considered. |
| first_indexed | 2024-04-12T06:41:35Z |
| format | Article |
| id | doaj.art-691f7a25fbc74ffc9b6f2667287d2284 |
| institution | Directory Open Access Journal |
| issn | 2411-1473 |
| language | Russian |
| last_indexed | 2024-04-12T06:41:35Z |
| publishDate | 2022-03-01 |
| publisher | The Fund for Promotion of Internet media, IT education, human development «League Internet Media» |
| record_format | Article |
| series | Современные информационные технологии и IT-образование |
| spelling | doaj.art-691f7a25fbc74ffc9b6f2667287d22842022-12-22T03:43:42ZrusThe Fund for Promotion of Internet media, IT education, human development «League Internet Media»Современные информационные технологии и IT-образование2411-14732022-03-0118110711810.25559/SITITO.18.202201.107-118Place of Neural Network Methods for Predicting Power Consumption in Railway TransportValeriya Osipova0https://orcid.org/0000-0002-7080-8756Dmitry Yakovlev1https://orcid.org/0000-0001-5926-392XIrkutsk State Transport University, Irkutsk, RussiaIrkutsk State Transport University, Irkutsk, RussiaThe article shows the relevance of forecasting electricity consumption using an automated electric energy metering system that takes into account various levels of the hierarchy of the power supply system. The features of modern management of fuel and energy resources of the enterprise are considered, using the example of the Trans-Baikal Directorate for Energy Supply ‒ a structural subdivision of Transenergo ‒ a branch of the open joint stock company "Russian Railways". The rational use of fuel and energy resources, the introduction of energy-saving and resource–saving technologies are among the most relevant both in Russia as a whole and in railway transport in particular. The specifics of Russia's economic development and the specifics of the formation of tariffs in the electric energy market have led to an increase in its cost. In this regard, reducing the cost of purchasing fuel and energy resources is one of the main goals of the energy strategy of railway transport. The result of the energy-saving policy of railway transport, in almost all components of the consumption of fuel and energy resources, excluding train traction, is a reduction in the cost of buying electric energy in the situation of reforming electric energy by entering the wholesale electric energy market (RE). It makes possible to reduce the cost of the consumed energy significantly, so the cost of electricity received by the railway from the EPR will be greatly lower than the cost of electricity received from the guaranteeing supplier ‒ the regional power system. The paper analyzes the factors affecting on the amount of electric energy consumption for train traction. Neural network models with high approximating ability are considered, which allow processing statistical information and performing predictive estimates. It is shown that multilayer neural networks should be considered the most acceptable for predicting power consumption. The method of neural network forecasting of electric energy consumption is considered.http://sitito.cs.msu.ru/index.php/SITITO/article/view/829neural networkspower consumption forecastingdirect distribution networkperceptronrailwaytrain traction |
| spellingShingle | Valeriya Osipova Dmitry Yakovlev Place of Neural Network Methods for Predicting Power Consumption in Railway Transport Современные информационные технологии и IT-образование neural networks power consumption forecasting direct distribution network perceptron railway train traction |
| title | Place of Neural Network Methods for Predicting Power Consumption in Railway Transport |
| title_full | Place of Neural Network Methods for Predicting Power Consumption in Railway Transport |
| title_fullStr | Place of Neural Network Methods for Predicting Power Consumption in Railway Transport |
| title_full_unstemmed | Place of Neural Network Methods for Predicting Power Consumption in Railway Transport |
| title_short | Place of Neural Network Methods for Predicting Power Consumption in Railway Transport |
| title_sort | place of neural network methods for predicting power consumption in railway transport |
| topic | neural networks power consumption forecasting direct distribution network perceptron railway train traction |
| url | http://sitito.cs.msu.ru/index.php/SITITO/article/view/829 |
| work_keys_str_mv | AT valeriyaosipova placeofneuralnetworkmethodsforpredictingpowerconsumptioninrailwaytransport AT dmitryyakovlev placeofneuralnetworkmethodsforpredictingpowerconsumptioninrailwaytransport |