Predictive Analytics for Increasing the Energy Efficiency of Industrial Enterprises-Reference-Cited by-同舟云学术

Predictive Analytics for Increasing the Energy Efficiency of Industrial Enterprises

Published:2022-04-28 Issue: Volume: Page:
ISSN:
Container-title:2022 International Conference on Smart Information Systems and Technologies (SIST)
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Author:

Kiyko Sergey¹^ORCID,Druzhinin Evgeniy²^ORCID,Prokhorov Oleksandr³^ORCID,Kosenko Viktor⁴^ORCID,Biletskyi Ihor⁵^ORCID,Kovalevskyi Mykhailo²^ORCID

Affiliation:

1. PJSC “Electrometallurgical plant “Dniprospetsstal” named after A. M. Kuzmin,Zaporozhye,Ukraine

2. National Aerospace University “Kharkiv Aviation Institute”,Department of Information Technologies of Design,Kharkiv,Ukraine

3. National Aerospace University “Kharkiv Aviation Institute”,Department of Computer Science and Information Technologies,Kharkiv,Ukraine

4. National University «Yuri Kondratyuk Poltava Polytechnic»,Department of Automation, Electronic and Telecommunication,Poltava,Ukraine

5. Institute of Energy, Information and Transport Infrastructure, O.M. Beketov National University of Urban Economy in Kharkiv,Kharkiv,Ukraine

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9945699/9945700/09945720.pdf?arnumber=9945720

Reference15 articles.

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3. Energy consumption forecasting based on Elman neural networks with evolutive optimization

4. Energy Consumption Forecasting for the Nonferrous Metallurgy Industry Using Hybrid Support Vector Regression with an Adaptive State Transition Algorithm;huang;Cognitive Computation,2019

5. Multi-agent Model of Energy Consumption at the Metallurgical Enterprise