Machine learning based analysis of factory energy load curves with focus on transition times for anomaly detection

Dominik Flick*, Claudio Keck, Christoph Herrmann, Sebastian Thiede

*Corresponding author for this work

Research output: Contribution to journalConference articleAcademicpeer-review

2 Downloads (Pure)

Abstract

An accurate understanding of energy load curves is the key for effective management of factory energy systems and basis for several energy applications (e.g. forecasts, anomaly detection). While load curve analysis has been a research topic with practical significance in many areas, there is a lack of methods particularly to evaluate different temporal transitions between energy states. Consequently, related energy saving potentials on factory level remain undetected. Against this background, the paper presents a methodology combining unsupervised univariate clustering and multivariate prediction based methods. Within an automotive use case for anomaly detection in energy performance management, those methods are getting applied and validated with real factory data.

Original languageEnglish
Pages (from-to)461-466
Number of pages6
JournalProcedia CIRP
Volume93
DOIs
Publication statusPublished - 22 Sep 2020
Externally publishedYes
Event53rd CIRP Conference on Manufacturing Systems, CIRP CMS 2020 - Virtual Conference, Chicago, United States
Duration: 1 Jul 20203 Jul 2020
Conference number: 53

Keywords

  • Clustering
  • Energy state and transition estimation
  • Prediction based variance analysis

Fingerprint Dive into the research topics of 'Machine learning based analysis of factory energy load curves with focus on transition times for anomaly detection'. Together they form a unique fingerprint.

Cite this