Enhancing power consumption prediction in steel manufacturing with hybrid classification and optimization models
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Bo Gu
Abstract
In the steel industry, the propensity of byproduct gas flow fluctuation is a critical factor in energy scheduling. The financial success of the steel industry will greatly benefit from an accurate forecast of its future tendencies. Manufacturers began to focus more on the needs of the client than on the product. Energy-intensive businesses like steel manufacturing are also affected by it. It might be difficult for mass customization firms to determine a product’s overall cost with accuracy. Moreover, energy accounts for 20 %–40 % of the expenditures associated with steel goods. Growing the selection of products may inevitably result in a loss of sustainability. This study investigates the prediction of power consumption in the steel industry by employing Linear Discriminant Analysis Classification (LDAC) and Decision Tree Classification (DTC). To improve the predictive accuracy of these foundational models, two advanced optimizers were utilized: The Electrostatic Discharge Algorithm (EDA) and the Political Optimizer (PO). These optimizers were systematically integrated with the base models to develop innovative hybrid models. Specifically, the LDAC model coupled with EDA forms the LDED framework, the LDAC model combined with PO constitutes the LDPO framework, the DTC model integrated with EDA results in the DTED framework, and the DTC model combined with PO creates the DTPO framework. The DTPO model performs exceptionally well in the Accuracy metric value at the Test section (value of 0.931), while the DTC and DTED models perform similarly well (both with a value of 0.911). In the Precision metric value at the Test section (value of 0.932), the DTPO model performs the best, and the LDED model performs the worst (value of 0.857).
Acknowledgments
I would like to take this opportunity to acknowledge that there are no individuals or organizations that require acknowledgment for their contributions to this work.
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Research ethics: Research involving Human Participants and Animals: The observational study conducted on medical staff needs no ethical code. Therefore, the above study was not required to acquire ethical code.
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Informed consent: This option is not necessary due to that the data were collected from the references.
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Author contributions: Authors’ contributions: The author contributed to the study’s conception and design. Data collection, simulation and analysis were performed by “Bo Gu”. Also the first draft of the manuscript was written by Bo Gu commented on previous versions of the manuscript.
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Use of Large Language Models, AI and Machine Learning Tools: During the preparation of this work, the authors used Large Language Models, AI, and Machine Learning tools for tasks such as language refinement, data analysis, or figure generation, with all outputs being reviewed and validated by the authors to ensure accuracy and originality. After using these tools/services, the authors reviewed and edited the content and take full responsibility for the content of the published article.
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Conflict of interest: The author declare no competing of interests.
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Research funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
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Data availability: The author do not have permissions to share data.
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