Short-term forecasting of consumption of the oil and gas enterprises using technological factors and Shapley additive explanations
RELEVANCE of the study lies in the development of system for the short-term forecasting of power consumption by the enterprise of the oil and gas industry with consideration of technological factors and interpretation of their influence on the result of the forecast.THE PURPOSE. To consider the prob...
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Kazan State Power Engineering University
2024-09-01
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| Series: | Известия высших учебных заведений: Проблемы энергетики |
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| Online Access: | https://www.energyret.ru/jour/article/view/3103 |
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| author | A. I. Stepanova A. I. Khalyasmaa P. V. Matrenin |
| author_facet | A. I. Stepanova A. I. Khalyasmaa P. V. Matrenin |
| author_sort | A. I. Stepanova |
| collection | DOAJ |
| description | RELEVANCE of the study lies in the development of system for the short-term forecasting of power consumption by the enterprise of the oil and gas industry with consideration of technological factors and interpretation of their influence on the result of the forecast.THE PURPOSE. To consider the problems of short-term forecasting. To test the applicability of the multi-agent approach to determine the features used to build a machine learning model of short-term forecasting of power consumption. To build machine learning models. To study the influence of technological factors on the accuracy of forecasting of power consumption. To apply the SHapley Additive exPlanations and analyze its interpretation of the forecasting results.METHODS. Pre-processing of the dataset, construction and testing of machine learning models were made in the programming language Python 3 using opensource libraries Scikit-Learn, XGBoost, LightGBM, Shap.RESULTS. The article describes the relevance of the topic of short-term forecasting of power consumption by the enterprise of the oil and gas industry within the ESG-approach. The method of selecting the features used using a multi-agent approach to build a machine learning model was developed. Machine learning models were built. Experimentations with the consideration of different features were made. Interpretation of results using SHapley Additive exPlanations was made.CONCLUSION. The use of technological factors of power consumption of compressor yards and natural gas air coolers allowed to increase the accuracy of forecast of power consumption from 8.82 % to 3.65 %. The application of the SHapley Additive exPlanations allows to interpret the results of machine learning models and confirms the need to consider technological factors in the task of short-term forecasting of power consumption of oil and gas industry. |
| format | Article |
| id | doaj-art-8fb9f8e41e00417ea8d67c7f9b2dd881 |
| institution | OA Journals |
| issn | 1998-9903 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Kazan State Power Engineering University |
| record_format | Article |
| series | Известия высших учебных заведений: Проблемы энергетики |
| spelling | doaj-art-8fb9f8e41e00417ea8d67c7f9b2dd8812025-08-20T02:27:27ZengKazan State Power Engineering UniversityИзвестия высших учебных заведений: Проблемы энергетики1998-99032024-09-01264758810.30724/1998-9903-2024-26-4-75-881032Short-term forecasting of consumption of the oil and gas enterprises using technological factors and Shapley additive explanationsA. I. Stepanova0A. I. Khalyasmaa1P. V. Matrenin2Ural Federal University named after the first President of Russia B. N. YeltsinUral Federal University named after the first President of Russia B. N. YeltsinUral Federal University named after the first President of Russia B. N. YeltsinRELEVANCE of the study lies in the development of system for the short-term forecasting of power consumption by the enterprise of the oil and gas industry with consideration of technological factors and interpretation of their influence on the result of the forecast.THE PURPOSE. To consider the problems of short-term forecasting. To test the applicability of the multi-agent approach to determine the features used to build a machine learning model of short-term forecasting of power consumption. To build machine learning models. To study the influence of technological factors on the accuracy of forecasting of power consumption. To apply the SHapley Additive exPlanations and analyze its interpretation of the forecasting results.METHODS. Pre-processing of the dataset, construction and testing of machine learning models were made in the programming language Python 3 using opensource libraries Scikit-Learn, XGBoost, LightGBM, Shap.RESULTS. The article describes the relevance of the topic of short-term forecasting of power consumption by the enterprise of the oil and gas industry within the ESG-approach. The method of selecting the features used using a multi-agent approach to build a machine learning model was developed. Machine learning models were built. Experimentations with the consideration of different features were made. Interpretation of results using SHapley Additive exPlanations was made.CONCLUSION. The use of technological factors of power consumption of compressor yards and natural gas air coolers allowed to increase the accuracy of forecast of power consumption from 8.82 % to 3.65 %. The application of the SHapley Additive exPlanations allows to interpret the results of machine learning models and confirms the need to consider technological factors in the task of short-term forecasting of power consumption of oil and gas industry.https://www.energyret.ru/jour/article/view/3103analysis of system properties and connectionsstructural analysis of the oil and gas industry enterprisemachine learningincrease of energy efficiencyshort-term forecasting of power consumptionshapley additive explanations |
| spellingShingle | A. I. Stepanova A. I. Khalyasmaa P. V. Matrenin Short-term forecasting of consumption of the oil and gas enterprises using technological factors and Shapley additive explanations Известия высших учебных заведений: Проблемы энергетики analysis of system properties and connections structural analysis of the oil and gas industry enterprise machine learning increase of energy efficiency short-term forecasting of power consumption shapley additive explanations |
| title | Short-term forecasting of consumption of the oil and gas enterprises using technological factors and Shapley additive explanations |
| title_full | Short-term forecasting of consumption of the oil and gas enterprises using technological factors and Shapley additive explanations |
| title_fullStr | Short-term forecasting of consumption of the oil and gas enterprises using technological factors and Shapley additive explanations |
| title_full_unstemmed | Short-term forecasting of consumption of the oil and gas enterprises using technological factors and Shapley additive explanations |
| title_short | Short-term forecasting of consumption of the oil and gas enterprises using technological factors and Shapley additive explanations |
| title_sort | short term forecasting of consumption of the oil and gas enterprises using technological factors and shapley additive explanations |
| topic | analysis of system properties and connections structural analysis of the oil and gas industry enterprise machine learning increase of energy efficiency short-term forecasting of power consumption shapley additive explanations |
| url | https://www.energyret.ru/jour/article/view/3103 |
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