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.
|Number of pages||6|
|Publication status||Published - 22 Sep 2020|
|Event||53rd CIRP Conference on Manufacturing Systems, CIRP CMS 2020 - Virtual Conference, Chicago, United States|
Duration: 1 Jul 2020 → 3 Jul 2020
Conference number: 53
- Energy state and transition estimation
- Prediction based variance analysis