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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Veröffentlicht in:IEEE International Conference on Computer Communication and the Internet (Online) S. 206 - 210
Hauptverfasser: Ji, Wanzhou, Bian, Yunqi, Cai, Hongxia
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 14.06.2024
Schlagworte:
Computational modeling
Deep Learning
Energy conservation
Energy consumption
Energy Consumption Prediction
Energy efficiency
Low-Code Development Platform
Prediction algorithms
Predictive models
Steel industry
ISSN:2833-2350
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Zusammenfassung: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.
ISSN:2833-2350
DOI:10.1109/ICCCI62159.2024.10674628