Forecasting material quantity using machine learning and times series techniques

H. Zermane, Hassina Madjour, Ahcene Ziar, Abderrahim Zermane
{"title":"Forecasting material quantity using machine learning and times series techniques","authors":"H. Zermane, Hassina Madjour, Ahcene Ziar, Abderrahim Zermane","doi":"10.2478/jee-2024-0029","DOIUrl":null,"url":null,"abstract":"Abstract The current research is dedicated to harnessing cutting-edge technologies within the paradigm of Industry 5.0. The objective is to capitalize on advancements in Machine and Deep Learning techniques. This research endeavors to construct robust predictive models, utilizing historical data, for precise real-time predictions in estimating material quantities within a cement workshop. Machine Learning regressors evaluated based on several metrics, SVR (R-squared 0.9739, MAE 0.0403), Random Forest (R-squared 0.9990, MAE 0.0026), MLP (R-squared 0.9890, MAE 0.0255), Gradient Boosting (R-squared 0.9989, MAE 0.0042). The time series models LSTM and GRU yielded R-squared 0.9978, MAE 0.0100, and R-squared 0.9980, MAE 0.0099, respectively. The ultimate outcomes include improved and efficient production, optimization of production processes, streamlined operations, reduced downtime, mitigation of potential disruptions, and the facilitation of the factory’s evolution towards intelligent manufacturing processes embedded within the framework of Industry 5.0. These achievements underscore the potential impact of leveraging advanced machine learning techniques for enhancing the operational dynamics and overall efficiency of manufacturing facilities","PeriodicalId":508697,"journal":{"name":"Journal of Electrical Engineering","volume":"15 1","pages":"237 - 248"},"PeriodicalIF":0.0000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Electrical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2478/jee-2024-0029","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Abstract The current research is dedicated to harnessing cutting-edge technologies within the paradigm of Industry 5.0. The objective is to capitalize on advancements in Machine and Deep Learning techniques. This research endeavors to construct robust predictive models, utilizing historical data, for precise real-time predictions in estimating material quantities within a cement workshop. Machine Learning regressors evaluated based on several metrics, SVR (R-squared 0.9739, MAE 0.0403), Random Forest (R-squared 0.9990, MAE 0.0026), MLP (R-squared 0.9890, MAE 0.0255), Gradient Boosting (R-squared 0.9989, MAE 0.0042). The time series models LSTM and GRU yielded R-squared 0.9978, MAE 0.0100, and R-squared 0.9980, MAE 0.0099, respectively. The ultimate outcomes include improved and efficient production, optimization of production processes, streamlined operations, reduced downtime, mitigation of potential disruptions, and the facilitation of the factory’s evolution towards intelligent manufacturing processes embedded within the framework of Industry 5.0. These achievements underscore the potential impact of leveraging advanced machine learning techniques for enhancing the operational dynamics and overall efficiency of manufacturing facilities
利用机器学习和时间序列技术预测材料数量
摘要 当前的研究致力于利用工业 5.0 范式中的尖端技术。目的是利用机器学习和深度学习技术的进步。本研究致力于利用历史数据构建稳健的预测模型,以便对水泥车间内的材料数量进行精确的实时预测。机器学习回归器根据多个指标进行了评估,包括 SVR(R-squared 0.9739,MAE 0.0403)、Random Forest(R-squared 0.9990,MAE 0.0026)、MLP(R-squared 0.9890,MAE 0.0255)、梯度提升(Gradient Boosting)(R-squared 0.9989,MAE 0.0042)。时间序列模型 LSTM 和 GRU 的 R 平方为 0.9978,MAE 为 0.0100,R 平方为 0.9980,MAE 为 0.0099。最终成果包括提高了生产效率、优化了生产流程、简化了操作、减少了停机时间、降低了潜在的干扰,以及促进了工厂向工业 5.0 框架内的智能制造流程发展。这些成就凸显了利用先进的机器学习技术提高生产设施的运行动态和整体效率的潜在影响
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信