Application of adaptive ensemble neural network method for short-term load forecasting electrical engineering complex of regional electric grid
The article is devoted to the problem of improving the accuracy of short-term load forecasting of electrical engineering complex of regional electric grid with the use deep machine learning tools. The effectiveness of the application of the adaptive learning algorithm for deep neural networks f...
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| Format: | Article |
| Language: | English |
| Published: |
Omsk State Technical University, Federal State Autonoumos Educational Institution of Higher Education
2021-03-01
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| Series: | Омский научный вестник |
| Subjects: | |
| Online Access: | https://www.omgtu.ru/general_information/media_omgtu/journal_of_omsk_research_journal/files/arhiv/2021/%20%E2%84%96%201%20(175)%20(%D0%9E%D0%9D%D0%92)/39-45%20%D0%A1%D0%B5%D1%80%D0%B5%D0%B1%D1%80%D1%8F%D0%BA%D0%BE%D0%B2%20%D0%9D.%20%D0%90..pdf |
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| _version_ | 1832574001449271296 |
|---|---|
| author | N. A. Serebryakov |
| author_facet | N. A. Serebryakov |
| author_sort | N. A. Serebryakov |
| collection | DOAJ |
| description | The article is devoted to the problem of improving the
accuracy of short-term load forecasting of electrical
engineering complex of regional electric grid with the
use deep machine learning tools. The effectiveness of
the application of the adaptive learning algorithm for
deep neural networks for short-term load forecasting of
this electrical complex has been investigated. The issues
of application of convolutional and recurrent neural
networks for short-term load forecasting are considered.
A comparative analysis of the accuracy of the short-term
load forecasting of electrical engineering complex of
regional electric grid obtained using the ensemble neural
network method and single neural networks are produced. |
| format | Article |
| id | doaj-art-d1e91d746a2b49198333e437f646f943 |
| institution | Kabale University |
| issn | 1813-8225 2541-7541 |
| language | English |
| publishDate | 2021-03-01 |
| publisher | Omsk State Technical University, Federal State Autonoumos Educational Institution of Higher Education |
| record_format | Article |
| series | Омский научный вестник |
| spelling | doaj-art-d1e91d746a2b49198333e437f646f9432025-02-02T01:57:02ZengOmsk State Technical University, Federal State Autonoumos Educational Institution of Higher EducationОмский научный вестник1813-82252541-75412021-03-011 (175)394510.25206/1813-8225-2021-175-39-45Application of adaptive ensemble neural network method for short-term load forecasting electrical engineering complex of regional electric gridN. A. Serebryakov0https://orcid.org/0000-0001-7428-7364Polzunov Altai State Technical UniversityThe article is devoted to the problem of improving the accuracy of short-term load forecasting of electrical engineering complex of regional electric grid with the use deep machine learning tools. The effectiveness of the application of the adaptive learning algorithm for deep neural networks for short-term load forecasting of this electrical complex has been investigated. The issues of application of convolutional and recurrent neural networks for short-term load forecasting are considered. A comparative analysis of the accuracy of the short-term load forecasting of electrical engineering complex of regional electric grid obtained using the ensemble neural network method and single neural networks are produced.https://www.omgtu.ru/general_information/media_omgtu/journal_of_omsk_research_journal/files/arhiv/2021/%20%E2%84%96%201%20(175)%20(%D0%9E%D0%9D%D0%92)/39-45%20%D0%A1%D0%B5%D1%80%D0%B5%D0%B1%D1%80%D1%8F%D0%BA%D0%BE%D0%B2%20%D0%9D.%20%D0%90..pdfregional electric gridforecasting electricity consumptionartificial neural networklearning algorithmconvolution networksrecurrent neural networks |
| spellingShingle | N. A. Serebryakov Application of adaptive ensemble neural network method for short-term load forecasting electrical engineering complex of regional electric grid Омский научный вестник regional electric grid forecasting electricity consumption artificial neural network learning algorithm convolution networks recurrent neural networks |
| title | Application of adaptive ensemble neural network method for short-term load forecasting electrical engineering complex of regional electric grid |
| title_full | Application of adaptive ensemble neural network method for short-term load forecasting electrical engineering complex of regional electric grid |
| title_fullStr | Application of adaptive ensemble neural network method for short-term load forecasting electrical engineering complex of regional electric grid |
| title_full_unstemmed | Application of adaptive ensemble neural network method for short-term load forecasting electrical engineering complex of regional electric grid |
| title_short | Application of adaptive ensemble neural network method for short-term load forecasting electrical engineering complex of regional electric grid |
| title_sort | application of adaptive ensemble neural network method for short term load forecasting electrical engineering complex of regional electric grid |
| topic | regional electric grid forecasting electricity consumption artificial neural network learning algorithm convolution networks recurrent neural networks |
| url | https://www.omgtu.ru/general_information/media_omgtu/journal_of_omsk_research_journal/files/arhiv/2021/%20%E2%84%96%201%20(175)%20(%D0%9E%D0%9D%D0%92)/39-45%20%D0%A1%D0%B5%D1%80%D0%B5%D0%B1%D1%80%D1%8F%D0%BA%D0%BE%D0%B2%20%D0%9D.%20%D0%90..pdf |
| work_keys_str_mv | AT naserebryakov applicationofadaptiveensembleneuralnetworkmethodforshorttermloadforecastingelectricalengineeringcomplexofregionalelectricgrid |