Operational Forecasting of Wind Speed for an Self-Contained Power Assembly of a Traction Substation
Currently, the prospects of creating hybrid power assemblies using renewable energy sources, including wind energy, and energy storage systems based on hydrogen energy technologies are being considered. To control such an energy storage system, it is necessary to perform operational renewable source...
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| Format: | Article |
| Language: | Russian |
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Belarusian National Technical University
2023-02-01
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| Series: | Известия высших учебных заведений и энергетических объединенний СНГ: Энергетика |
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| Online Access: | https://energy.bntu.by/jour/article/view/2230 |
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| author | P. V. Matrenin A. I. Khalyasmaa A. G. Rusina S. A. Eroshenko N. A. Papkova D. A. Sekatski |
| author_facet | P. V. Matrenin A. I. Khalyasmaa A. G. Rusina S. A. Eroshenko N. A. Papkova D. A. Sekatski |
| author_sort | P. V. Matrenin |
| collection | DOAJ |
| description | Currently, the prospects of creating hybrid power assemblies using renewable energy sources, including wind energy, and energy storage systems based on hydrogen energy technologies are being considered. To control such an energy storage system, it is necessary to perform operational renewable sources generation forecasting, particularly forecasting of wind power assemblies. Their production depends on the speed and direction of the wind. The article presents the results of solving the problem of operational forecasting of wind speed for a hybrid power assembly project aimed at increasing the capacity of the railway section between Yaya and Izhmorskaya stations (Kemerovo region of the Russian Federation). Hourly data of wind speeds and directions for 15 years have been analyzed, a neural network model has been built, and a compact architecture of a multilayer perceptron has been proposed for short-term forecasting of wind speed and direction for 1 and 6 hours ahead. The model that has been developed allows minimizing the risks of overfitting and loss of forecasting accuracy due to changes in the operating conditions of the model over time. A specific feature of this work is the stability investigation of the model trained on the data of long-term observations to long-term changes, as well as the analysis of the possibilities of improving the accuracy of forecasting due to regular further training of the model on newly available data. The nature of the influence of the size of the training sample and the self-adaptation of the model on the accuracy of forecasting and the stability of its work on the horizon of several years has been established. It is shown that in order to ensure high accuracy and stability of the neural network model of wind speed forecasting, long-term meteorological observations data are required. |
| format | Article |
| id | doaj-art-eb93869559564c5e98bf7daf07d17560 |
| institution | Kabale University |
| issn | 1029-7448 2414-0341 |
| language | Russian |
| publishDate | 2023-02-01 |
| publisher | Belarusian National Technical University |
| record_format | Article |
| series | Известия высших учебных заведений и энергетических объединенний СНГ: Энергетика |
| spelling | doaj-art-eb93869559564c5e98bf7daf07d175602025-02-03T11:34:18ZrusBelarusian National Technical UniversityИзвестия высших учебных заведений и энергетических объединенний СНГ: Энергетика1029-74482414-03412023-02-01661182910.21122/1029-7448-2023-66-1-18-291837Operational Forecasting of Wind Speed for an Self-Contained Power Assembly of a Traction SubstationP. V. Matrenin0A. I. Khalyasmaa1A. G. Rusina2S. A. Eroshenko3N. A. Papkova4D. A. Sekatski5Novosibirsk State Technical University; Ural Federal University named after the first President of Russia B. N. YeltsinNovosibirsk State Technical University; Ural Federal University named after the first President of Russia B. N. YeltsinNovosibirsk State Technical UniversityNovosibirsk State Technical University; Ural Federal University named after the first President of Russia B. N. YeltsinBelаrusian National Technical UniversityBelаrusian National Technical UniversityCurrently, the prospects of creating hybrid power assemblies using renewable energy sources, including wind energy, and energy storage systems based on hydrogen energy technologies are being considered. To control such an energy storage system, it is necessary to perform operational renewable sources generation forecasting, particularly forecasting of wind power assemblies. Their production depends on the speed and direction of the wind. The article presents the results of solving the problem of operational forecasting of wind speed for a hybrid power assembly project aimed at increasing the capacity of the railway section between Yaya and Izhmorskaya stations (Kemerovo region of the Russian Federation). Hourly data of wind speeds and directions for 15 years have been analyzed, a neural network model has been built, and a compact architecture of a multilayer perceptron has been proposed for short-term forecasting of wind speed and direction for 1 and 6 hours ahead. The model that has been developed allows minimizing the risks of overfitting and loss of forecasting accuracy due to changes in the operating conditions of the model over time. A specific feature of this work is the stability investigation of the model trained on the data of long-term observations to long-term changes, as well as the analysis of the possibilities of improving the accuracy of forecasting due to regular further training of the model on newly available data. The nature of the influence of the size of the training sample and the self-adaptation of the model on the accuracy of forecasting and the stability of its work on the horizon of several years has been established. It is shown that in order to ensure high accuracy and stability of the neural network model of wind speed forecasting, long-term meteorological observations data are required.https://energy.bntu.by/jour/article/view/2230wind speed forecastingwind powerrailway electrification systemneural networks |
| spellingShingle | P. V. Matrenin A. I. Khalyasmaa A. G. Rusina S. A. Eroshenko N. A. Papkova D. A. Sekatski Operational Forecasting of Wind Speed for an Self-Contained Power Assembly of a Traction Substation Известия высших учебных заведений и энергетических объединенний СНГ: Энергетика wind speed forecasting wind power railway electrification system neural networks |
| title | Operational Forecasting of Wind Speed for an Self-Contained Power Assembly of a Traction Substation |
| title_full | Operational Forecasting of Wind Speed for an Self-Contained Power Assembly of a Traction Substation |
| title_fullStr | Operational Forecasting of Wind Speed for an Self-Contained Power Assembly of a Traction Substation |
| title_full_unstemmed | Operational Forecasting of Wind Speed for an Self-Contained Power Assembly of a Traction Substation |
| title_short | Operational Forecasting of Wind Speed for an Self-Contained Power Assembly of a Traction Substation |
| title_sort | operational forecasting of wind speed for an self contained power assembly of a traction substation |
| topic | wind speed forecasting wind power railway electrification system neural networks |
| url | https://energy.bntu.by/jour/article/view/2230 |
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