A Rank Analysis and Ensemble Machine Learning Model for Load Forecasting in the Nodes of the Central Mongolian Power System
Электронный научный архив УРФУ
Информация об архиве | Просмотр оригинала| Поле | Значение | |
| Заглавие |
A Rank Analysis and Ensemble Machine Learning Model for Load Forecasting in the Nodes of the Central Mongolian Power System
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| Автор |
Osgonbaatar, T.
Matrenin, P. Safaraliev, M. Zicmane, I. Rusina, A. Kokin, S. |
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| Тематика |
CENTRAL POWER SYSTEM OF MONGOLIA
DAILY LOAD SCHEDULE FORECASTING MACHINE LEARNING NODE SUBSTATIONS POWER SUPPLY ZONE RANK MODELS |
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| Описание |
Forecasting electricity consumption is currently one of the most important scientific and practical tasks in the field of electric power industry. The early retrieval of data on expected load profiles makes it possible to choose the optimal operating mode of the system. The resultant forecast accuracy significantly affects the performance of the entire electrical complex and the operating conditions of the electricity market. This can be achieved through using a model of total electricity consumption designed with an acceptable margin of error. This paper proposes a new method for predicting power consumption in all nodes of the power system through the determination of rank coefficients calculated directly for the corresponding voltage level, including node substations, power supply zones, and other parts of the power system. The forecast of the daily load schedule and the construction of a power consumption model was based on the example of nodes in the central power system in Mongolia. An ensemble of decision trees was applied to construct a daily load schedule and rank coefficients were used to simulate consumption in the nodes. Initial data were obtained from daily load schedules, meteorological factors, and calendar features of the central power system, which accounts for the majority of energy consumption and generation in Mongolia. The study period was 2019–2021. The daily load schedules of the power system were constructed using machine learning with a probability of 1.25%. The proposed rank analysis for power system zones increases the forecasting accuracy for each zone and can improve the quality of management and create more favorable conditions for the development of distributed generation. © 2023 by the authors.
Ministry of Education and Science of the Russian Federation, Minobrnauka The research funding from the Ministry of Science and Higher Education of the Russian Federation (Ural Federal University Program of Development within the Priority-2030 Program) is gratefully acknowledged. |
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| Дата |
2024-04-05T16:35:00Z
2024-04-05T16:35:00Z 2023 |
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| Тип |
Article
Journal article (info:eu-repo/semantics/article) |info:eu-repo/semantics/publishedVersion |
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| Идентификатор |
Osgonbaatar, T, Matrenin, P, Safaraliev, M, Zicmane, I, Rusina, A & Kokin, S 2023, 'A Rank Analysis and Ensemble Machine Learning Model for Load Forecasting in the Nodes of the Central Mongolian Power System', Inventions, Том. 8, № 5, 114. https://doi.org/10.3390/inventions8050114
Osgonbaatar, T., Matrenin, P., Safaraliev, M., Zicmane, I., Rusina, A., & Kokin, S. (2023). A Rank Analysis and Ensemble Machine Learning Model for Load Forecasting in the Nodes of the Central Mongolian Power System. Inventions, 8(5), [114]. https://doi.org/10.3390/inventions8050114 2411-5134 Final All Open Access, Gold https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175027418&doi=10.3390%2finventions8050114&partnerID=40&md5=4748a5575a18af6337ce2978ee1075e0 https://www.mdpi.com/2411-5134/8/5/114/pdf?version=1693911622 http://elar.urfu.ru/handle/10995/130887 10.3390/inventions8050114 85175027418 001095068700001 |
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| Язык |
en
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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 |
Multidisciplinary Digital Publishing Institute (MDPI)
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| Источник |
Inventions
Inventions |
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