Fuzzy logic and machine learning methods applied to the analysis of industrial power consumption under the condition of uncertainty
Introduction. Recently, the fuzzy logic method has been widely implemented in solving various problems of economic research, including theoretical analysis of the economy and its resource dependence, the study of innovative processes in a resource-type economy. Purpose. The purpose of the research...
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| Format: | Article |
| Language: | Russian |
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Perm State University
2024-04-01
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| Series: | Вестник Пермского университета: Серия Экономика |
| Online Access: | https://economics.psu.ru:443/index.php/econ/article/view/537 |
| _version_ | 1797206477064110080 |
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| author | Leonid Aleksandrovich Serkov |
| author_facet | Leonid Aleksandrovich Serkov |
| author_sort | Leonid Aleksandrovich Serkov |
| collection | DOAJ |
| description | Introduction. Recently, the fuzzy logic method has been widely implemented in solving various problems of economic research, including theoretical analysis of the economy and its resource dependence, the study of innovative processes in a resource-type economy.
Purpose. The purpose of the research is to analyze the dependence of industrial power consumption from various social economic factors with the fuzzy modeling method. This method is particularly well suited for modeling ill-defined systems with the significant uncertainty about the nature and range of key input variables and the underlying relationships between them. This system could be illustrated by the economy of modern Russia at the time of sanctions imposed by unfriendly states.
Materials and methods. The work refers to fuzzy modeling and machine learning methods. A random forest algorithm was used to select predictors and for comparative analysis.
Results. The results of fuzzy modeling were compared with the results obtained by modeling the analyzed relationship with multiple regression, and with the results obtained by applying the random forest method with regression decision trees to the data under study. Fuzzy logic-based modeling of the above-described dependence in the context of uncertainty is shown to be more adequate compared to regression-based modeling (including the random forest method).
Conclusion. The proposed fuzzy system (fuzzy inference system) can be used to study the influence of changes in any input factor or their combination on changes in industrial power consumption. The fuzzy system could reveal how much various production locations could change industrial electricity consumption or analyze the feasibility of a location in terms of access to labor resources. It is also possible to study how much the number of employees associated with the outflow of labor resources could change industrial electricity consumption. |
| first_indexed | 2024-04-24T09:07:38Z |
| format | Article |
| id | doaj.art-d995c6c4450646068ccc15c33099f21f |
| institution | Directory Open Access Journal |
| issn | 1994-9960 2658-624X |
| language | Russian |
| last_indexed | 2024-04-24T09:07:38Z |
| publishDate | 2024-04-01 |
| publisher | Perm State University |
| record_format | Article |
| series | Вестник Пермского университета: Серия Экономика |
| spelling | doaj.art-d995c6c4450646068ccc15c33099f21f2024-04-15T18:40:17ZrusPerm State UniversityВестник Пермского университета: Серия Экономика1994-99602658-624X2024-04-0119152–6852–6810.17072/1994-9960-2024-1-52-68537Fuzzy logic and machine learning methods applied to the analysis of industrial power consumption under the condition of uncertaintyLeonid Aleksandrovich Serkov0Институт экономики Уральского отделения Российской академии наукIntroduction. Recently, the fuzzy logic method has been widely implemented in solving various problems of economic research, including theoretical analysis of the economy and its resource dependence, the study of innovative processes in a resource-type economy. Purpose. The purpose of the research is to analyze the dependence of industrial power consumption from various social economic factors with the fuzzy modeling method. This method is particularly well suited for modeling ill-defined systems with the significant uncertainty about the nature and range of key input variables and the underlying relationships between them. This system could be illustrated by the economy of modern Russia at the time of sanctions imposed by unfriendly states. Materials and methods. The work refers to fuzzy modeling and machine learning methods. A random forest algorithm was used to select predictors and for comparative analysis. Results. The results of fuzzy modeling were compared with the results obtained by modeling the analyzed relationship with multiple regression, and with the results obtained by applying the random forest method with regression decision trees to the data under study. Fuzzy logic-based modeling of the above-described dependence in the context of uncertainty is shown to be more adequate compared to regression-based modeling (including the random forest method). Conclusion. The proposed fuzzy system (fuzzy inference system) can be used to study the influence of changes in any input factor or their combination on changes in industrial power consumption. The fuzzy system could reveal how much various production locations could change industrial electricity consumption or analyze the feasibility of a location in terms of access to labor resources. It is also possible to study how much the number of employees associated with the outflow of labor resources could change industrial electricity consumption.https://economics.psu.ru:443/index.php/econ/article/view/537 |
| spellingShingle | Leonid Aleksandrovich Serkov Fuzzy logic and machine learning methods applied to the analysis of industrial power consumption under the condition of uncertainty Вестник Пермского университета: Серия Экономика |
| title | Fuzzy logic and machine learning methods applied to the analysis of industrial power consumption under the condition of uncertainty |
| title_full | Fuzzy logic and machine learning methods applied to the analysis of industrial power consumption under the condition of uncertainty |
| title_fullStr | Fuzzy logic and machine learning methods applied to the analysis of industrial power consumption under the condition of uncertainty |
| title_full_unstemmed | Fuzzy logic and machine learning methods applied to the analysis of industrial power consumption under the condition of uncertainty |
| title_short | Fuzzy logic and machine learning methods applied to the analysis of industrial power consumption under the condition of uncertainty |
| title_sort | fuzzy logic and machine learning methods applied to the analysis of industrial power consumption under the condition of uncertainty |
| url | https://economics.psu.ru:443/index.php/econ/article/view/537 |
| work_keys_str_mv | AT leonidaleksandrovichserkov fuzzylogicandmachinelearningmethodsappliedtotheanalysisofindustrialpowerconsumptionundertheconditionofuncertainty |