Realization of Low-Code Algorithm for Steel Energy Consumption Prediction
The steel industry is one of the world's highest energy consumption. It is important to improve its energy efficiency. Energy consumption prediction can help steel industry better plan and manage energy use. Based on a low-code development platform, multiple prediction algorithm models are used...
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| Vydáno v: | IEEE International Conference on Computer Communication and the Internet (Online) s. 206 - 210 |
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| Jazyk: | angličtina |
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IEEE
14.06.2024
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| ISSN: | 2833-2350 |
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| Abstract | The steel industry is one of the world's highest energy consumption. It is important to improve its energy efficiency. Energy consumption prediction can help steel industry better plan and manage energy use. Based on a low-code development platform, multiple prediction algorithm models are used to predict the energy consumption. A low-code algorithmic platform support is provided for the steel industry to achieve energy saving and efficiency improvement. |
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| AbstractList | The steel industry is one of the world's highest energy consumption. It is important to improve its energy efficiency. Energy consumption prediction can help steel industry better plan and manage energy use. Based on a low-code development platform, multiple prediction algorithm models are used to predict the energy consumption. A low-code algorithmic platform support is provided for the steel industry to achieve energy saving and efficiency improvement. |
| Author | Bian, Yunqi Cai, Hongxia Ji, Wanzhou |
| Author_xml | – sequence: 1 givenname: Wanzhou surname: Ji fullname: Ji, Wanzhou email: 15161971811@shu.edu.cn organization: School of Mechatronic Engineering and Automation Shanghai University,Shanghai Key Laboratory of Intelligent Manufacturing and Robotics,Shanghai,China – sequence: 2 givenname: Yunqi surname: Bian fullname: Bian, Yunqi email: bianyunqi@shu.edu.cn organization: School of Mechatronic Engineering and Automation Shanghai University,Shanghai Key Laboratory of Intelligent Manufacturing and Robotics,Shanghai,China – sequence: 3 givenname: Hongxia surname: Cai fullname: Cai, Hongxia email: hxcai@staff.shu.edu.cn organization: School of Mechatronic Engineering and Automation Shanghai University,Shanghai Key Laboratory of Intelligent Manufacturing and Robotics,Shanghai,China |
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| Snippet | The steel industry is one of the world's highest energy consumption. It is important to improve its energy efficiency. Energy consumption prediction can help... |
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| StartPage | 206 |
| SubjectTerms | Computational modeling Deep Learning Energy conservation Energy consumption Energy Consumption Prediction Energy efficiency Low-Code Development Platform Prediction algorithms Predictive models Steel industry |
| Title | Realization of Low-Code Algorithm for Steel Energy Consumption Prediction |
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