| Autoencoder-based anomaly root cause analysis for wind turbines CMA Roelofs, MA Lutz, S Faulstich, S Vogt Energy and AI 4, 100065, 2021 | 52 | 2021 |
| Transfer learning applications for autoencoder-based anomaly detection in wind turbines CMA Roelofs, C Gück, S Faulstich Energy and AI 17, 100373, 2024 | 2 | 2024 |
| AI agents assessing flexibility: the value of demand side management in times of high energy prices A Dreher, LM Martmann, M Lehna, C Roelofs, J Bergsträßer, C Scholz, ... 2022 18th International Conference on the European Energy Market (EEM), 1-9, 2022 | 1 | 2022 |
| CARE to Compare: A real-world dataset for anomaly detection in wind turbine data C Gück, C Roelofs, S Faulstich arXiv preprint arXiv:2404.10320, 2024 | | 2024 |
| Transfer learning applications for anomaly detection in wind turbines C Roelofs, C Gück, S Faulstich arXiv preprint arXiv:2404.03011, 2024 | | 2024 |
| ADWENTURE-Anomaly Detection for Wind Turbine Efficiency CMA Roelofs, C Gück, E Guevara Bastidas, F Rehwald | | 2024 |
| CARE to Compare: A Real-World Benchmark Dataset for Early Fault Detection in Wind Turbine Data C Gück, CMA Roelofs, S Faulstich Data 9 (12), 138, 2024 | | 2024 |
