Medium-term forecasting of power generation by hydropower plants in isolated power systems under climate change
Электронный научный архив УРФУ
Информация об архиве | Просмотр оригинала| Поле | Значение | |
| Заглавие |
Medium-term forecasting of power generation by hydropower plants in isolated power systems under climate change
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| Автор |
Safaraliev, M.
Kiryanova, N. Matrenin, P. Dmitriev, S. Kokin, S. Kamalov, F. |
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| Тематика |
CLIMATE CHANGE
ENSEMBLE MODELS GBAO HYDROPOWER PLANT ISOLATED POWER SYSTEM MEDIUM-TERM FORECASTING OF POWER GENERATION TEMPERATURE ADAPTIVE BOOSTING CLIMATE CHANGE CLIMATE MODELS DECISION TREES ENERGY UTILIZATION HYDROELECTRIC POWER HYDROELECTRIC POWER PLANTS LEARNING SYSTEMS MACHINE LEARNING NEAREST NEIGHBOR SEARCH STOCHASTIC SYSTEMS WIND ENSEMBLE MODELS GBAO HYDROPOWER PLANTS ISOLATED POWER SYSTEM MACHINE LEARNING MODELS MEDIUM TERM MEDIUM-TERM FORECASTING OF POWER GENERATION POWER- GENERATIONS RELIABLE OPERATION STOCHASTICS FORECASTING |
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| Описание |
Reliable operation of power systems (PS), including those with a significant share of hydropower plants (HPPs) in the energy balance, largely depends on the accuracy of forecasting power generation. The importance of power generation forecasts increases with the development of renewable power generation, which is stochastic by nature. Those kinds of tasks are complicated by the lack of reliable information on metrological data and estimated energy consumption, which is also stochastic. In the medium-term forecasting (MTF) of power generation by HPPs, the seasonality of changes in flow and inflow of water should be taken into account, which significantly affects the reserves and regulatory capabilities of the power system as a whole. This work discusses the problem of constructing a model for MTF of power generation HPP in isolated power systems (IPS), taking into account such atmospheric parameters as air temperature, wind speed and humidity. To address constant climatic changes, this paper suggests implementing machine learning models. The proposed approach is characterized by a high degree of autonomy and learning automation. The paper provides a comparative study of the machine learning models such as polynomial model with Tikhonov's regularization (LR), k-nearest neighbors (kNN), multilayer perceptron (MLP), ensembles of decision trees, adaptive boosting of linear models (ABLR), etc. Computational experiments have shown that the machine learning approach yields the results of sufficient quality, which allows to use them for forecasting of power generation HPP in isolated power systems under conditions of climate change. The Adaptive Boosting Linear Regression model is the simplest and most reliable machine learning model that has proven itself well in the tasks with a relatively small amount of training samples. © 2022 The Author(s)
Novosibirsk State Technical University, NSTU, (C22-15) The study was financially supported as part of the Novosibirsk State Technical University, Russia development program, scientific project C22-15. |
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| Дата |
2024-04-22T15:52:30Z
2024-04-22T15:52:30Z 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 |
Safaraliev, M, Kiryanova, N, Matrenin, P, Dmitriev, S, Kokin, S & Kamalov, F 2022, 'Medium-term forecasting of power generation by hydropower plants in isolated power systems under climate change', Energy Reports, Том. 8, стр. 765-774. https://doi.org/10.1016/j.egyr.2022.09.164
Safaraliev, M., Kiryanova, N., Matrenin, P., Dmitriev, S., Kokin, S., & Kamalov, F. (2022). Medium-term forecasting of power generation by hydropower plants in isolated power systems under climate change. Energy Reports, 8, 765-774. https://doi.org/10.1016/j.egyr.2022.09.164 2352-4847 Final All Open Access; Gold Open Access https://doi.org/10.1016/j.egyr.2022.09.164 https://doi.org/10.1016/j.egyr.2022.09.164 http://elar.urfu.ru/handle/10995/132319 10.1016/j.egyr.2022.09.164 85140083937 886228300015 |
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| Язык |
en
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| Права |
Open access (info:eu-repo/semantics/openAccess)
cc-by-nc-nd |
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| Формат |
application/pdf
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| Издатель |
Elsevier Ltd
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| Источник |
Energy Reports
Energy Reports |
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