Improving of the Generation Accuracy Forecasting of Photovoltaic Plants Based on k-Means and k-Nearest Neighbors Algorithms
Электронный научный архив УРФУ
Информация об архиве | Просмотр оригинала| Поле | Значение | |
| Заглавие |
Improving of the Generation Accuracy Forecasting of Photovoltaic Plants Based on k-Means and k-Nearest Neighbors Algorithms
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| Автор |
Matrenin, P. V.
Khalyasmaa, A. I. Gamaley, V. V. Eroshenko, S. A. Papkova, N. A. Sekatski, D. A. Potachits, Y. V. |
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| Тематика |
ADAPTIVE BOOSTING
DATA CLUSTERING DATA PREPROCESSING ELECTRICITY GENERATION INSOLATION LINEAR REGRESSION MACHINE LEARNING METEOROLOGICAL FACTORS NEURAL NETWORKS PHOTOVOLTAIC PLANT PREDICTIVE MODEL PRINCIPAL COMPONENT ANALYSIS RENEWABLE ENERGY SOURCES SHORT-TERM FORECASTING SOLAR RADIATION |
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| Описание |
Renewable energy sources (RES) are seen as a means of the fuel and energy complex carbon footprint reduction but the stochastic nature of generation complicates RES integration with electric power systems. Therefore, it is necessary to develop and improve methods for forecasting of the power plants generation using the energy of the sun, wind and water flows. One of the ways to improve the accuracy of forecast models is a deep analysis of meteorological conditions as the main factor affecting the power generation. In this paper, a method for adapting of forecast models to the meteorological conditions of photovoltaic stations operation based on machine learning algorithms was proposed and studied. In this case, unsupervised learning is first performed using the k-means method to form clusters. For this, it is also proposed to use studied the feature space dimensionality reduction algorithm to visualize and estimate the clustering accuracy. Then, for each cluster, its own machine learning model was trained for generation forecasting and the k-nearest neighbours algorithm was built to attribute the current conditions at the model operation stage to one of the formed clusters. The study was conducted on hourly meteorological data for the period from 1985 to 2021. A feature of the approach is the clustering of weather conditions on hourly rather than daily intervals. As a result, the mean absolute percentage error of forecasting is reduced significantly, depending on the prediction model used. For the best case, the error in forecasting of a photovoltaic plant generation an hour ahead was 9 %. © 2023 Belarusian National Technical University. All rights reserved.
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| Дата |
2024-04-05T16:34:09Z
2024-04-05T16:34:09Z 2023 |
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| Тип |
Article
Journal article (info:eu-repo/semantics/article) |info:eu-repo/semantics/publishedVersion |
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| Идентификатор |
Matrenin, PV, Khalyasmaa, AI, Gamaley, VV, Eroshenko, SA, Papkova, NA, Sekatski, DA & Potachits, YV 2023, 'Повышение точности прогнозирования генерации фотоэлектрических станций на основе алгоритмов k-средних и k-ближайших соседей', Energetika. Proceedings of CIS Higher Education Institutions and Power Engineering Associations, Том. 66, № 4, стр. 305-321. https://doi.org/10.21122/1029-7448-2023-66-4-305-321, https://doi.org/10.21122/1029-7448-2023-66-4
Matrenin, P. V., Khalyasmaa, A. I., Gamaley, V. V., Eroshenko, S. A., Papkova, N. A., Sekatski, D. A., & Potachits, Y. V. (2023). Повышение точности прогнозирования генерации фотоэлектрических станций на основе алгоритмов k-средних и k-ближайших соседей. Energetika. Proceedings of CIS Higher Education Institutions and Power Engineering Associations, 66(4), 305-321. https://doi.org/10.21122/1029-7448-2023-66-4-305-321, https://doi.org/10.21122/1029-7448-2023-66-4 1029-7448 Final All Open Access, Gold, Green https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173264860&doi=10.21122%2f1029-7448-2023-66-4-305-321&partnerID=40&md5=3997b1ec6b643692ae961c91930f5606 https://energy.bntu.by/jour/article/download/2287/1876 http://elar.urfu.ru/handle/10995/130841 10.21122/1029-7448-2023-66-4-305-321 85173264860 |
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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 |
Belarusian National Technical University
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| Источник |
ENERGETIKA. Proceedings of CIS higher education institutions and power engineering associations
Energetika. Proceedings of CIS Higher Education Institutions and Power Engineering Associations |
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