Monthly Runoff Forecasting by Non-Generalizing Machine Learning Model and Feature Space Transformation (Vakhsh River Case Study)
Электронный научный архив УРФУ
Информация об архиве | Просмотр оригинала| Поле | Значение | |
| Заглавие |
Monthly Runoff Forecasting by Non-Generalizing Machine Learning Model and Feature Space Transformation (Vakhsh River Case Study)
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| Автор |
Matrenin, P. V.
Safaraliev, M. K. Kiryanova, N. G. Sultonov, S. M. |
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| Тематика |
CASCADE OF HYDROPOWER PLANTS
GENERATION PLANNING HYDROPOWER LONG-TERM FORECASTING MACHINE LEARNING REPUBLIC OF TAJIKISTAN RIVER FLOW |
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| Описание |
Energy prices and сost of materials for solar and wind power plants have increased over the past year. Therefore, significance increases for the hydropower and long-term (1-10 years) planning generation for the existing hydropower plants, which requires forecasting the average monthly values of the river flow. This task is especially urgent for countries without their own oil-fields and opportunity to invest in the creation of solar or wind power plants. The aim of the research is to decrease the mean absolute forecasting error of the long-term prediction for the Vakhsh River flow (Tajikistan) based on the long-term observations. A study of existing methods for the river runoff forecasting in relation to the object under consideration was carried out, and a new transformation model for the space of the input features was developed. The most significant results are the decrease in the average forecast error in the Vakhsh river flow achieved by the use of the proposed space of polynomial logarithmic features in comparison with other methods, and the need to use at least the 20 year-old observational data for the long-term operation planning of the hydropower plants and cascades of the hydropower plants obtained from the results of computational experiments. The significance of the results lies in the fact that a new approach to the long-term forecasting of river flow has been proposed and verified using the long-term observations. This approach does not require the use of the long-term meteorological forecasts, which are not possible to obtain with high accuracy for all regions. © 2022 Problems of the Regional Energetics. All rights reserved.
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| Дата |
2024-04-08T11:07:08Z
2024-04-08T11:07:08Z 2022 |
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| Тип |
Article
Journal article (info:eu-repo/semantics/article) Published version (info:eu-repo/semantics/publishedVersion) |
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| Идентификатор |
Matrenin, PV, Safaraliev, MK, Kiryanova, NG & Sultonov, SM 2022, 'Прогнозирование среднемесячных значений стоков рек с применением необобщающей модели машинного обучения и преобразованием пространства признаков (на примере реки Вахш)', Problems of the Regional Energetics, № 3, стр. 99-110. https://doi.org/10.52254/1857-0070.2022.3-55.08
Matrenin, P. V., Safaraliev, M. K., Kiryanova, N. G., & Sultonov, S. M. (2022). Прогнозирование среднемесячных значений стоков рек с применением необобщающей модели машинного обучения и преобразованием пространства признаков (на примере реки Вахш). Problems of the Regional Energetics, (3), 99-110. https://doi.org/10.52254/1857-0070.2022.3-55.08 1857-0070 Final All Open Access; Gold Open Access https://doi.org/10.52254/1857-0070.2022.3-55.08 https://doi.org/10.52254/1857-0070.2022.3-55.08 http://elar.urfu.ru/handle/10995/131410 10.52254/1857-0070.2022.3-55.08 85138811938 000892787700012 |
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| Язык |
ru
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| Права |
Open access (info:eu-repo/semantics/openAccess)
cc-by https://creativecommons.org/licenses/by/4.0/ |
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| Формат |
application/pdf
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| Издатель |
Institute of Power Engineering
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| Источник |
Problems of the Regional Energetics
Problems of the Regional Energetics |
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