Variant regression modeling of electricity production in the Russian Federation
Objective. The aim of the study is to build a linear regression model of electricity generation in the Russian Federation depending on resource indicators, which include: the volume of coal and gas production, the production of fuel oil. Statistical data for 2005 - 2020 were used as the information...
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| Format: | Article |
| Language: | Russian |
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Dagestan State Technical University
2023-05-01
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| Series: | Вестник Дагестанского государственного технического университета: Технические науки |
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| Online Access: | https://vestnik.dgtu.ru/jour/article/view/1224 |
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| author | S. I. Noskov E. S. Popov S. P. Seredkin V. V. Tirskikh V. D. Toropov |
| author_facet | S. I. Noskov E. S. Popov S. P. Seredkin V. V. Tirskikh V. D. Toropov |
| author_sort | S. I. Noskov |
| collection | DOAJ |
| description | Objective. The aim of the study is to build a linear regression model of electricity generation in the Russian Federation depending on resource indicators, which include: the volume of coal and gas production, the production of fuel oil. Statistical data for 2005 - 2020 were used as the information base of the study.Method. Estimation of unknown parameters of the linear model is carried out using three methods - least squares, modules and anti-robust estimation. They behave differently with respect to outliers in the data. The second of them does not react to outliers at all, completely ignoring them, and the third, on the contrary, strongly gravitates towards them, therefore, these methods are a kind of antagonists in relation to each other.Result. Three alternative models of a linear regression model of electricity production with high accuracy are obtained. The value of the parametric stability index of the data sample, based on the properties of the parameter estimation methods, is calculated. Observations are identified that correspond to the maximum and minimum extent to the linear model on the analyzed sample. The values of the contributions of the factors to the right parts of the models are calculated.Conclusion. Three versions of the model built by different methods can be successfully used to solve problems related to forecasting the production of electricity in the country. At the same time, the variant constructed by the least squares method is a kind of compromise. |
| format | Article |
| id | doaj-art-40cf85ef47ef4977a0df635ed3c84b05 |
| institution | DOAJ |
| issn | 2073-6185 2542-095X |
| language | Russian |
| publishDate | 2023-05-01 |
| publisher | Dagestan State Technical University |
| record_format | Article |
| series | Вестник Дагестанского государственного технического университета: Технические науки |
| spelling | doaj-art-40cf85ef47ef4977a0df635ed3c84b052025-08-20T02:55:54ZrusDagestan State Technical UniversityВестник Дагестанского государственного технического университета: Технические науки2073-61852542-095X2023-05-0150112312910.21822/2073-6185-2023-50-1-123-129765Variant regression modeling of electricity production in the Russian FederationS. I. Noskov0E. S. Popov1S. P. Seredkin2V. V. Tirskikh3V. D. Toropov4Irkutsk State Transport UniversityIrkutsk State Transport UniversityIrkutsk State Transport UniversityIrkutsk State Transport UniversityBaikal State UniversityObjective. The aim of the study is to build a linear regression model of electricity generation in the Russian Federation depending on resource indicators, which include: the volume of coal and gas production, the production of fuel oil. Statistical data for 2005 - 2020 were used as the information base of the study.Method. Estimation of unknown parameters of the linear model is carried out using three methods - least squares, modules and anti-robust estimation. They behave differently with respect to outliers in the data. The second of them does not react to outliers at all, completely ignoring them, and the third, on the contrary, strongly gravitates towards them, therefore, these methods are a kind of antagonists in relation to each other.Result. Three alternative models of a linear regression model of electricity production with high accuracy are obtained. The value of the parametric stability index of the data sample, based on the properties of the parameter estimation methods, is calculated. Observations are identified that correspond to the maximum and minimum extent to the linear model on the analyzed sample. The values of the contributions of the factors to the right parts of the models are calculated.Conclusion. Three versions of the model built by different methods can be successfully used to solve problems related to forecasting the production of electricity in the country. At the same time, the variant constructed by the least squares method is a kind of compromise.https://vestnik.dgtu.ru/jour/article/view/1224electricity generationlinear regression modelleast squaresmoduliantirobust estimationparametric homogeneity indexfactor contributions |
| spellingShingle | S. I. Noskov E. S. Popov S. P. Seredkin V. V. Tirskikh V. D. Toropov Variant regression modeling of electricity production in the Russian Federation Вестник Дагестанского государственного технического университета: Технические науки electricity generation linear regression model least squares moduli antirobust estimation parametric homogeneity index factor contributions |
| title | Variant regression modeling of electricity production in the Russian Federation |
| title_full | Variant regression modeling of electricity production in the Russian Federation |
| title_fullStr | Variant regression modeling of electricity production in the Russian Federation |
| title_full_unstemmed | Variant regression modeling of electricity production in the Russian Federation |
| title_short | Variant regression modeling of electricity production in the Russian Federation |
| title_sort | variant regression modeling of electricity production in the russian federation |
| topic | electricity generation linear regression model least squares moduli antirobust estimation parametric homogeneity index factor contributions |
| url | https://vestnik.dgtu.ru/jour/article/view/1224 |
| work_keys_str_mv | AT sinoskov variantregressionmodelingofelectricityproductionintherussianfederation AT espopov variantregressionmodelingofelectricityproductionintherussianfederation AT spseredkin variantregressionmodelingofelectricityproductionintherussianfederation AT vvtirskikh variantregressionmodelingofelectricityproductionintherussianfederation AT vdtoropov variantregressionmodelingofelectricityproductionintherussianfederation |