Machine learning based internal and external energy assessment of automotive factories

被引：2

作者：

Flick, Dominik ^{[1
,2
]}

Vruna, Melina ^{[2
]}

Bartos, Milan ^{[3
]}

Ji, Li ^{[2
]}

Herrmann, Christoph ^{[1
]}

Thiede, Sebastian ^{[4
]}

机构：

[1] Tech Univ Carolo Wilhelmina Braunschweig, Inst Machine Tools & Prod Technol, D-38106 Braunschweig, Germany

[2] Stellantis NV, Global Mfg Decarbonisat Engn, NL-2132 LS Hoofddorp, Netherlands

[3] Toyota Motor Mfg Czech Republ sro, Kolin 28002, Czech Republic

[4] Univ Twente, Chair Mfg Syst, Dept Design Prod & Management, Enschede, Netherlands

来源：

CIRP ANNALS-MANUFACTURING TECHNOLOGY | 2023年 / 72卷 / 01期

关键词：

Energyefficiency; Factory; Machine learning; BENCHMARKING; EFFICIENCY;

D O I：

10.1016/j.cirp.2023.04.038

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In order to reduce industrial greenhouse gas emissions, systematic energy demand analysis and the derivation of improvement strategies are key. Against this background, a methodology for data driven energy demand prediction and performance benchmarking for factories is presented. The machine learning based approach enables to quantify performance influencing factors, identify "best in class" factories and fields of action for improvement. The results are validated within an automotive OEM internal and even external competitor assessment. The transferable approach based on well accessible public data also enables larger industry wide studies. & COPY; 2023 The Author(s). Published by Elsevier Ltd on behalf of CIRP. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

引用

页码：21 / 24

页数：4

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