1.2k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of KasselDE13 papers
- AIT Austrian Institute of Technology GmbHAT7 papers
- National Technical University of AthensGR6 papers
- Delft University of TechnologyNL5 papers
- Technical University of DenmarkDK5 papers
- European Distributed Energy Resources LaboratoriesDE4 papers
- Oldenburger Institut für InformatikDE4 papers
- University of StrathclydeGB4 papers
- HAW HamburgDE3 papers
- Laboratoire de Génie Électrique de GrenobleFR3 papers
- Tennet (Netherlands)NL3 papers
- Université Grenoble AlpesFR3 papers
5 papers · 1 filter
Graph Reinforcement Learning for Power Grids: A Comprehensive Survey
Mohamed Hassouna, Clara Holzhüter, Pawel Lytaev +3
The increasing share of renewable energy and distributed electricity generation requires the development of deep learning approaches to address the lack of flexibility inherent in…
Achievements, Experiences, and Lessons Learned from the European Research Infrastructure ERIGrid related to the Validation of Power and Energy Systems
T. I. Strasser, E. C. W. de Jong, M. Sosnina +9
Power system operation is of vital importance and must be developed far beyond today's practice to meet future needs. Almost all European countries are facing an abrupt and very im…
Quantifying seasonal hydrogen storage demands under cost and market uptake uncertainties in energy system transformation pathways
Felix Frischmuth, Mattis Berghoff, Martin Braun +1
Climate neutrality paradigms put electricity systems at the core of a clean energy supply. At the same time, indirect electrification, with a potential uptake of hydrogen or derive…
CARE to Compare: A real-world dataset for anomaly detection in wind turbine data
Christian Gück, Cyriana M. A. Roelofs, Stefan Faulstich
Anomaly detection plays a crucial role in the field of predictive maintenance for wind turbines, yet the comparison of different algorithms poses a difficult task because domain sp…
Transfer learning applications for anomaly detection in wind turbines
Cyriana M. A. Roelofs, Christian Gück, Stefan Faulstich
Anomaly detection in wind turbines typically involves using normal behaviour models to detect faults early. However, training autoencoder models for each turbine is time-consuming…