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Models of Operational Forecasting of Arc Furnace Energy Consumption Using Machine Learning Methods: book chapter. / Saidmurodov, Begmurod; Zyuzev, Anatoly; Lyukhanov, Egor.
Proceedings - 2023 Russian Workshop on Power Engineering and Automation of Metallurgy Industry: Research and Practice, PEAMI 2023: book. Institute of Electrical and Electronics Engineers Inc., 2023. p. 205-210.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Harvard

Saidmurodov, B, Zyuzev, A & Lyukhanov, E 2023, Models of Operational Forecasting of Arc Furnace Energy Consumption Using Machine Learning Methods: book chapter. in Proceedings - 2023 Russian Workshop on Power Engineering and Automation of Metallurgy Industry: Research and Practice, PEAMI 2023: book. Institute of Electrical and Electronics Engineers Inc., pp. 205-210, 2023 Russian Workshop on Power Engineering and Automation of Metallurgy Industry: Research & Practice (PEAMI), 29/09/2023. https://doi.org/10.1109/PEAMI58441.2023.10299891

APA

Saidmurodov, B., Zyuzev, A., & Lyukhanov, E. (2023). Models of Operational Forecasting of Arc Furnace Energy Consumption Using Machine Learning Methods: book chapter. In Proceedings - 2023 Russian Workshop on Power Engineering and Automation of Metallurgy Industry: Research and Practice, PEAMI 2023: book (pp. 205-210). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PEAMI58441.2023.10299891

Vancouver

Saidmurodov B, Zyuzev A, Lyukhanov E. Models of Operational Forecasting of Arc Furnace Energy Consumption Using Machine Learning Methods: book chapter. In Proceedings - 2023 Russian Workshop on Power Engineering and Automation of Metallurgy Industry: Research and Practice, PEAMI 2023: book. Institute of Electrical and Electronics Engineers Inc. 2023. p. 205-210 doi: 10.1109/PEAMI58441.2023.10299891

Author

Saidmurodov, Begmurod ; Zyuzev, Anatoly ; Lyukhanov, Egor. / Models of Operational Forecasting of Arc Furnace Energy Consumption Using Machine Learning Methods : book chapter. Proceedings - 2023 Russian Workshop on Power Engineering and Automation of Metallurgy Industry: Research and Practice, PEAMI 2023: book. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 205-210

BibTeX

@inproceedings{a5a8e9cf35c34ee08e6df9748788b7e6,
title = "Models of Operational Forecasting of Arc Furnace Energy Consumption Using Machine Learning Methods: book chapter",
abstract = "This paper is an overview of the current research in the field of predicting the energy consumption of electric arc furnaces with emphasis on literature review. Electric arc furnaces play a key role in the metallurgical and metalworking industries, and optimizing their energy consumption is a crucial task to improve production efficiency. This paper analyzes the state-of-the-art machine learning methods used to predict the energy consumption of electric arc furnaces. Special attention is given to the literature review, which is a comparative analysis of previous research in this area. Various methods including neural networks, regression analysis, time series and others are reviewed. The literature review not only provides an assessment of the current state of research, but also identifies limitations and potential directions for future research. The conclusions and recommendations presented in the paper can serve as a basis for further research in the field of optimizing energy consumption in electric arc furnaces and improving the efficiency of metallurgical production. {\textcopyright} 2023 IEEE.",
author = "Begmurod Saidmurodov and Anatoly Zyuzev and Egor Lyukhanov",
note = "I sincerely thank Ismoil Nazrimadovich Odinaev for his valuable comments and quality editing of the text, which significantly improved the readability and conciseness of the article. I also express my deep gratitude to Murodbek Kholnazarovich Safaraliev for providing access to the necessary data and information resources, which allowed for a more in-depth analysis of the research.; 2023 Russian Workshop on Power Engineering and Automation of Metallurgy Industry: Research & Practice (PEAMI) ; Conference date: 29-09-2023 Through 01-10-2023",
year = "2023",
doi = "10.1109/PEAMI58441.2023.10299891",
language = "English",
isbn = "979-835032215-6",
pages = "205--210",
booktitle = "Proceedings - 2023 Russian Workshop on Power Engineering and Automation of Metallurgy Industry: Research and Practice, PEAMI 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

RIS

TY - GEN

T1 - Models of Operational Forecasting of Arc Furnace Energy Consumption Using Machine Learning Methods

T2 - 2023 Russian Workshop on Power Engineering and Automation of Metallurgy Industry: Research & Practice (PEAMI)

AU - Saidmurodov, Begmurod

AU - Zyuzev, Anatoly

AU - Lyukhanov, Egor

N1 - I sincerely thank Ismoil Nazrimadovich Odinaev for his valuable comments and quality editing of the text, which significantly improved the readability and conciseness of the article. I also express my deep gratitude to Murodbek Kholnazarovich Safaraliev for providing access to the necessary data and information resources, which allowed for a more in-depth analysis of the research.

PY - 2023

Y1 - 2023

N2 - This paper is an overview of the current research in the field of predicting the energy consumption of electric arc furnaces with emphasis on literature review. Electric arc furnaces play a key role in the metallurgical and metalworking industries, and optimizing their energy consumption is a crucial task to improve production efficiency. This paper analyzes the state-of-the-art machine learning methods used to predict the energy consumption of electric arc furnaces. Special attention is given to the literature review, which is a comparative analysis of previous research in this area. Various methods including neural networks, regression analysis, time series and others are reviewed. The literature review not only provides an assessment of the current state of research, but also identifies limitations and potential directions for future research. The conclusions and recommendations presented in the paper can serve as a basis for further research in the field of optimizing energy consumption in electric arc furnaces and improving the efficiency of metallurgical production. © 2023 IEEE.

AB - This paper is an overview of the current research in the field of predicting the energy consumption of electric arc furnaces with emphasis on literature review. Electric arc furnaces play a key role in the metallurgical and metalworking industries, and optimizing their energy consumption is a crucial task to improve production efficiency. This paper analyzes the state-of-the-art machine learning methods used to predict the energy consumption of electric arc furnaces. Special attention is given to the literature review, which is a comparative analysis of previous research in this area. Various methods including neural networks, regression analysis, time series and others are reviewed. The literature review not only provides an assessment of the current state of research, but also identifies limitations and potential directions for future research. The conclusions and recommendations presented in the paper can serve as a basis for further research in the field of optimizing energy consumption in electric arc furnaces and improving the efficiency of metallurgical production. © 2023 IEEE.

UR - http://www.scopus.com/inward/record.url?partnerID=8YFLogxK&scp=85178519037

U2 - 10.1109/PEAMI58441.2023.10299891

DO - 10.1109/PEAMI58441.2023.10299891

M3 - Conference contribution

SN - 979-835032215-6

SP - 205

EP - 210

BT - Proceedings - 2023 Russian Workshop on Power Engineering and Automation of Metallurgy Industry: Research and Practice, PEAMI 2023

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 29 September 2023 through 1 October 2023

ER -

ID: 49270762