Machine learning based internal and external energy assessment of automotive factories

Dominik Flick, Melina Vruna, Milan Bartos, Li Ji, Christoph Herrmann, Sebastian Thiede*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

22 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)21-24
Number of pages4
JournalCIRP annals : manufacturing technology
Issue number1
Early online date2 Jun 2023
Publication statusPublished - 13 Jul 2023


  • Energy efficiency
  • Factory
  • Machine learning
  • UT-Hybrid-D


Dive into the research topics of 'Machine learning based internal and external energy assessment of automotive factories'. Together they form a unique fingerprint.

Cite this