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http://elar.urfu.ru/handle/10995/111797
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Поле DC | Значение | Язык |
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dc.contributor.author | Matrenin, P. | en |
dc.contributor.author | Safaraliev, M. | en |
dc.contributor.author | Dmitriev, S. | en |
dc.contributor.author | Kokin, S. | en |
dc.contributor.author | Eshchanov, B. | en |
dc.contributor.author | Rusina, A. | en |
dc.date.accessioned | 2022-05-12T08:23:02Z | - |
dc.date.available | 2022-05-12T08:23:02Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | Adaptive Ensemble Models for Medium-Term Forecasting of Water Inflow When Planning Electricity Generation under Climate Change / P. Matrenin, M. Safaraliev, S. Dmitriev et al. // Energy Reports. — 2022. — Vol. 8. — P. 439-447. | en |
dc.identifier.issn | 2352-4847 | - |
dc.identifier.other | All Open Access, Gold | 3 |
dc.identifier.uri | http://elar.urfu.ru/handle/10995/111797 | - |
dc.description.abstract | Medium-term forecasting of water inflow is of great importance for small hydroelectric power plants operating in remote power supply areas and having a small reservoir. Improving the forecasting accuracy is aimed at solving the problem of determining the water reserve for the future generation of electricity at hydroelectric power plants, taking into account the regulation in the medium term. Medium-term regulation is necessary to amplify the load in the peak and semi-peak portions of the load curve. The solution to such problems is aggravated by the lack of sufficiently reliable information on water inflow and prospective power consumption, which is of a stochastic nature. In addition, the mid-term planning of electricity generation should consider the seasonality of changes in water inflow, which directly affects the reserves and the possibility of regulation. The paper considers the problem of constructing a model for medium-term forecasting of water inflow for planning electricity generation, taking into account climatic changes in isolated power systems. Taking into account the regularly increasing effect of climate change, the current study proposes using an approach based on machine learning methods, which are distinguished by a high degree of autonomy and automation of learning, that is, the ability to self-adapt. The results showed that the error (RMSE) of the model based on the ensemble of regression decision trees due to constant self-adaptation decreased from 4.5 m3/s to 4.0 m3/s and turned out to be lower than the error of a more complex multilayer recurrent neural network (4.9 m3/s). The research results are intended to improve forecasting reliability in the planning, management, and operation of isolated operating power systems. © 2021 The Author(s). | en |
dc.description.sponsorship | The reported study was funded by RFBR, Sirius University of Science and Technology, JSC Russian Railways and Educational Fund “Talent and success”, project number 20-38-51007. | en |
dc.format.mimetype | application/pdf | en |
dc.language.iso | en | en |
dc.publisher | Elsevier Ltd | en1 |
dc.publisher | Elsevier BV | en |
dc.rights | info:eu-repo/semantics/openAccess | en |
dc.source | Energy Rep. | 2 |
dc.source | Energy Reports | en |
dc.subject | ELECTRIC POWER SYSTEM | en |
dc.subject | ENSEMBLE MODELS | en |
dc.subject | ISOLATED POWER SYSTEM | en |
dc.subject | MEDIUM-TERM FORECASTING | en |
dc.subject | SMALL HYDROPOWER PLANT | en |
dc.subject | WATER INFLOW | en |
dc.subject | CLIMATE CHANGE | en |
dc.subject | CLIMATE MODELS | en |
dc.subject | DECISION TREES | en |
dc.subject | ELECTRIC POWER SYSTEM PLANNING | en |
dc.subject | FORECASTING | en |
dc.subject | HYDROELECTRIC POWER PLANTS | en |
dc.subject | MULTILAYER NEURAL NETWORKS | en |
dc.subject | RECURRENT NEURAL NETWORKS | en |
dc.subject | RESERVOIRS (WATER) | en |
dc.subject | STOCHASTIC SYSTEMS | en |
dc.subject | ELECTRICITY-GENERATION | en |
dc.subject | ENSEMBLE MODELS | en |
dc.subject | FORECASTING ACCURACY | en |
dc.subject | ISOLATED POWER SYSTEM | en |
dc.subject | POWER SUPPLY | en |
dc.subject | REMOTE POWER | en |
dc.subject | SMALL HYDRO POWER PLANTS | en |
dc.subject | SMALL HYDROELECTRIC POWER PLANTS | en |
dc.subject | SMALL RESERVOIRS | en |
dc.subject | WATER INFLOWS | en |
dc.subject | HYDROELECTRIC POWER | en |
dc.title | Adaptive Ensemble Models for Medium-Term Forecasting of Water Inflow When Planning Electricity Generation under Climate Change | en |
dc.type | Article | en |
dc.type | info:eu-repo/semantics/article | en |
dc.type | info:eu-repo/semantics/publishedVersion | en |
dc.identifier.doi | 10.1016/j.egyr.2021.11.112 | - |
dc.identifier.scopus | 85120341646 | - |
local.contributor.employee | Matrenin, P., Department of Power Supply Systems, Novosibirsk State Technical University, Novosibirsk, Russian Federation, Sirius University of Science and Technology, Sochi, Russian Federation; Safaraliev, M., Department of Automated Electrical Systems, Ural Federal University, Yekaterinburg, Russian Federation; Dmitriev, S., Department of Automated Electrical Systems, Ural Federal University, Yekaterinburg, Russian Federation; Kokin, S., Department of Automated Electrical Systems, Ural Federal University, Yekaterinburg, Russian Federation; Eshchanov, B., Management Development Institute of Singapore in Tashkent, Tashkent, Uzbekistan; Rusina, A., Department of Power Stations, Novosibirsk State Technical University, Novosibirsk, Russian Federation, Sirius University of Science and Technology, Sochi, Russian Federation | en |
local.description.firstpage | 439 | - |
local.description.lastpage | 447 | - |
local.volume | 8 | - |
dc.identifier.wos | 000744124800006 | - |
local.contributor.department | Department of Power Supply Systems, Novosibirsk State Technical University, Novosibirsk, Russian Federation; Department of Automated Electrical Systems, Ural Federal University, Yekaterinburg, Russian Federation; Management Development Institute of Singapore in Tashkent, Tashkent, Uzbekistan; Department of Power Stations, Novosibirsk State Technical University, Novosibirsk, Russian Federation; Sirius University of Science and Technology, Sochi, Russian Federation | en |
local.identifier.pure | 29138924 | - |
local.identifier.eid | 2-s2.0-85120341646 | - |
local.fund.rffi | 20-38-51007 | - |
local.identifier.wos | WOS:000744124800006 | - |
Располагается в коллекциях: | Научные публикации ученых УрФУ, проиндексированные в SCOPUS и WoS CC |
Файлы этого ресурса:
Файл | Описание | Размер | Формат | |
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2-s2.0-85120341646.pdf | 1,02 MB | Adobe PDF | Просмотреть/Открыть |
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