@inproceedings{Kleiner2024,
  author    = {Kleiner, Carsten},
  title     = {Explainable Anomaly Detection in Renewable Energy Power Plants by Learning Multidimensional Normality Models},
  booktitle = {Proceedings of the Workshops of the EDBT/ICDT 2024 Joint Conference co-located with the EDBT/ICDT 2024 Joint Conference (CEUR Workshop Proceedings ; 3651)},
  issn      = {1613-0073},
  doi       = {10.25968/opus-3102},
  institution = {Fakult{\"a}t IV - Wirtschaft und Informatik},
  pages     = {8},
  year      = {2024},
  abstract  = {Renewable energy production is one of the strongest rising markets and further extreme growth can be anticipated due to desire of increased sustainability in many parts of the world. With the rising adoption of renewable power production, such facilities are increasingly attractive targets for cyber attacks. At the same time higher requirements on a reliable production are raised. In this paper we propose a concept that improves monitoring of renewable power plants by detecting anomalous behavior. The system does not only detect an anomaly, it also provides reasoning for the anomaly based on a specific mathematical model of the expected behavior by giving detailed information about various influential factors causing the alert. The set of influential factors can be configured into the system before learning normal behaviour. The concept is based on multidimensional analysis and has been implemented and successfully evaluated on actual data from different providers of wind power plants.},
  subject      = {Anomalieerkennung},
  language  = {en}
}