On the choice of the method of dynamic rationing of energy resources in oil refineries
The article discusses the possibility of calculating the expected energy demand based on big data and machine learning for the energy technological processes in oil refineries. In order to obtain predictive data, linear regression, machine learning, and neural networks are proposed to be used to b...
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| _version_ | 1832572793484476416 |
|---|---|
| author | V. R. Vedruchenko E. M. Rezanov A. P. Starikov A. V. Kushnarenko P. A. Surovtsev V. A. Kikhtenko |
| author_facet | V. R. Vedruchenko E. M. Rezanov A. P. Starikov A. V. Kushnarenko P. A. Surovtsev V. A. Kikhtenko |
| author_sort | V. R. Vedruchenko |
| collection | DOAJ |
| description | The article discusses the possibility of calculating the expected energy demand based on big data and
machine learning for the energy technological processes in oil refineries. In order to obtain predictive
data, linear regression, machine learning, and neural networks are proposed to be used to build
a mathematical model. The advantages and disadvantages of these methods are discussed, and the
accuracy of the models is compared with the possibility of interpreting them. Thanks to the use of
advanced statistical methods, the variability of energy consumption can be interpreted through factor
analysis. Through pilot tests, the practical significance of these proposed methods for their practical use
in an energy management system is demonstrated, as well as the transition to statistical control of the
process. |
| format | Article |
| id | doaj-art-207678577ed24b8d9783d279f3b2dbb1 |
| institution | Kabale University |
| issn | 2588-0373 2587-764X |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Omsk State Technical University, Federal State Autonomous Educational Institution of Higher Education |
| record_format | Article |
| series | Омский научный вестник: Серия "Авиационно-ракетное и энергетическое машиностроение" |
| spelling | doaj-art-207678577ed24b8d9783d279f3b2dbb12025-02-02T06:50:08ZengOmsk State Technical University, Federal State Autonomous Educational Institution of Higher EducationОмский научный вестник: Серия "Авиационно-ракетное и энергетическое машиностроение"2588-03732587-764X2024-06-018251210.25206/2588-0373-2024-8-2-5-12On the choice of the method of dynamic rationing of energy resources in oil refineriesV. R. Vedruchenko0E. M. Rezanov1A. P. Starikov2A. V. Kushnarenko3P. A. Surovtsev4V. A. Kikhtenko5Omsk State Transport UniversityOmsk State Transport UniversityOmsk State Transport UniversityOmsk State Transport UniversityOmsk State Transport UniversityOmsk State Transport UniversityThe article discusses the possibility of calculating the expected energy demand based on big data and machine learning for the energy technological processes in oil refineries. In order to obtain predictive data, linear regression, machine learning, and neural networks are proposed to be used to build a mathematical model. The advantages and disadvantages of these methods are discussed, and the accuracy of the models is compared with the possibility of interpreting them. Thanks to the use of advanced statistical methods, the variability of energy consumption can be interpreted through factor analysis. Through pilot tests, the practical significance of these proposed methods for their practical use in an energy management system is demonstrated, as well as the transition to statistical control of the process.https://www.omgtu.ru/general_information/media_omgtu/journal_of_omsk_research_journal/files/arhiv/2024/%D0%A2.8,%20%E2%84%962%20(%D0%90%D0%A0%D0%B8%D0%AD%D0%9C)/5-12%20%D0%92%D0%B5%D0%B4%D1%80%D1%83%D1%87%D0%B5%D0%BD%D0%BA%D0%BE%20%D0%92.%20%D0%A0.,%20%D0%A0%D0%B5%D0%B7%D0%B0%D0%BD%D0%BE%D0%B2%20%D0%95.%20%D0%9C.,%20%D0%A1%D1%82%D0%B0%D1%80%D0%B8%D0%BA%D0%BE%D0%B2%20%D0%90.%20%D0%9F.,%20%D0%9A%D1%83%D1%88%D0%BD%D0%B0%D1%80%D0%B5%D0%BD%D0%BA%D0%BE%20%D0%90.%20%D0%92.,%20%D0%A1%D1%83%D1%80%D0%BE%D0%B2%D1%86%D0%B5%D0%B2%20%D0%9F.%20%D0%90.,%20%D0%9A%D0%B8%D1%85%D1%82%D0%B5%D0%BD%D0%BA%D0%BE%20%D0%92.%20%D0%90..pdfrationing of energy resourcesfuel and energy resourcesrationing methodslinear regressionmachine learningdeep learningfactor analysisenergy management system |
| spellingShingle | V. R. Vedruchenko E. M. Rezanov A. P. Starikov A. V. Kushnarenko P. A. Surovtsev V. A. Kikhtenko On the choice of the method of dynamic rationing of energy resources in oil refineries Омский научный вестник: Серия "Авиационно-ракетное и энергетическое машиностроение" rationing of energy resources fuel and energy resources rationing methods linear regression machine learning deep learning factor analysis energy management system |
| title | On the choice of the method of dynamic rationing of energy resources in oil refineries |
| title_full | On the choice of the method of dynamic rationing of energy resources in oil refineries |
| title_fullStr | On the choice of the method of dynamic rationing of energy resources in oil refineries |
| title_full_unstemmed | On the choice of the method of dynamic rationing of energy resources in oil refineries |
| title_short | On the choice of the method of dynamic rationing of energy resources in oil refineries |
| title_sort | on the choice of the method of dynamic rationing of energy resources in oil refineries |
| topic | rationing of energy resources fuel and energy resources rationing methods linear regression machine learning deep learning factor analysis energy management system |
| url | https://www.omgtu.ru/general_information/media_omgtu/journal_of_omsk_research_journal/files/arhiv/2024/%D0%A2.8,%20%E2%84%962%20(%D0%90%D0%A0%D0%B8%D0%AD%D0%9C)/5-12%20%D0%92%D0%B5%D0%B4%D1%80%D1%83%D1%87%D0%B5%D0%BD%D0%BA%D0%BE%20%D0%92.%20%D0%A0.,%20%D0%A0%D0%B5%D0%B7%D0%B0%D0%BD%D0%BE%D0%B2%20%D0%95.%20%D0%9C.,%20%D0%A1%D1%82%D0%B0%D1%80%D0%B8%D0%BA%D0%BE%D0%B2%20%D0%90.%20%D0%9F.,%20%D0%9A%D1%83%D1%88%D0%BD%D0%B0%D1%80%D0%B5%D0%BD%D0%BA%D0%BE%20%D0%90.%20%D0%92.,%20%D0%A1%D1%83%D1%80%D0%BE%D0%B2%D1%86%D0%B5%D0%B2%20%D0%9F.%20%D0%90.,%20%D0%9A%D0%B8%D1%85%D1%82%D0%B5%D0%BD%D0%BA%D0%BE%20%D0%92.%20%D0%90..pdf |
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