Prediction of Manufacturing Plant's Electric Power Using Machine Learning

被引：0

作者：

Yeom, Kyoe-Rae ^{[1
]}

Choi, Hyo-Sub ^{[1
]}

机构：

[1] KETI Korea Elect Technol Inst, Embedded SW, 22,Daewangpangyo Ro 712beon Gil, Seongnam Si, Gyeonggi Do, South Korea

来源：

2018 TENTH INTERNATIONAL CONFERENCE ON UBIQUITOUS AND FUTURE NETWORKS (ICUFN 2018) | 2018年

关键词：

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, we apply the data accumulated through E-IOT platform to machine learning method to find significant variables first and predict the electric power generated in manufacturing process by using these variables. Pre-processing such as resampling of data was carried out before the prediction. In order to select the significant variables, 25 variables were derived using Lasso (least absolute shrinkage and selection operator), one of the machine learning techniques. We used Deep Learning 's LSTM technique, one of the field of machine learning for the prediction.

引用

页码：808 / 810

页数：3

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