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Simon Letzgus
Simon Letzgus
Verified email at tu-berlin.de
Title
Cited by
Cited by
Year
An artificial neural network‐based condition monitoring method for wind turbines, with application to the monitoring of the gearbox
P Bangalore, S Letzgus, D Karlsson, M Patriksson
Wind Energy 20 (8), 1421-1438, 2017
1352017
Toward explainable artificial intelligence for regression models: A methodological perspective
S Letzgus, P Wagner, J Lederer, W Samek, KR Müller, G Montavon
IEEE Signal Processing Magazine 39 (4), 40-58, 2022
802022
A sequential pressure-based algorithm for data-driven leakage identification and model-based localization in water distribution networks
I Daniel, J Pesantez, S Letzgus, MA Khaksar Fasaee, F Alghamdi, ...
Journal of Water Resources Planning and Management 148 (6), 04022025, 2022
272022
Change-point detection in wind turbine SCADA data for robust condition monitoring with normal behaviour models
S Letzgus
Wind Energy Science Discussions 2020, 1-29, 2020
192020
A GIS-Based planning approach for urban power and natural gas distribution grids with different heat pump scenarios
JM Kisse, M Braun, S Letzgus, TM Kneiske
Energies 13 (16), 4052, 2020
122020
Marktdesign, Regulierung und Gesamteffizienz von Flexibilität im Stromsystem–Bestandsaufnahme und Herausforderungen
H Kondziella, S Graupner, T Bruckner, H Doderer, S Schäfer-Stradowsky, ...
Accessed: Dec 11, 2020, 2019
82019
Enabling co-innovation for a successful digital transformation in wind energy using a new digital ecosystem and a fault detection case study
S Barber, LAM Lima, Y Sakagami, J Quick, E Latiffianti, Y Liu, R Ferrari, ...
Energies 15 (15), 5638, 2022
52022
A high-resolution pressure-driven method for leakage identification and localization in water distribution networks. Zenodo
I Daniel, J Pesantez, S Letzgus, MA Khaksar Fasaee, F Alghamdi, ...
52020
SCADA-data analysis for condition monitoring of wind turbines
S Letzgus
52015
A high-resolution pressure-driven method for leakage identification and localization in water distribution networks
I Daniel, J Pesantez, S Letzgus, MAK Fasaee, F Alghamdi, ...
Zenodo, 2020
42020
Marktdesign, Regulierung und Gesamteffizienz von Flexibilität im Stromsystem–Bestandsaufnahme und Herausforderungen [WindNODEArbeitspaket 5 „Marktdesign und Regulierung–neue …
H Kondziella, S Graupner, T Bruckner, H Doderer, S Schäfer-Stradowsky, ...
42019
An explainable AI framework for robust and transparent data-driven wind turbine power curve models
S Letzgus, KR Müller
Energy and AI 15, 100328, 2024
12024
Towards transparent ANN wind turbine power curve models.
S Letzgus
arXiv preprint arXiv:2210.12104, 2022
12022
XpertAI: uncovering model strategies for sub-manifolds
S Letzgus, KR Müller, G Montavon
arXiv preprint arXiv:2403.07486, 2024
2024
Towards transparent and robust data-driven wind turbine power curve models
S Letzgus, KR Müller
arXiv preprint arXiv:2304.09835, 2023
2023
XAI for transparent wind turbine power curve models
S Letzgus
arXiv preprint arXiv:2210.12104, 2022
2022
Training data requirements for SCADA based condition monitoring using artificial neural networks
S Letzgus
15th EAWE PhD Seminar on Wind Energy, 2019
2019
SCADA-based anomaly detection – challenges for automated application of artificial neural networks
S Letzgus
14th EAWE PhD Seminar on Wind Energy, 2018
2018
Integrated Intelligence Assessment For Energy Systems
S Letzgus, C Koch, G Erdmann
Transforming Energy Markets, 41st IAEE International Conference, Jun 10-13, 2018, 2018
2018
Energy and AI
S Letzgus, KR Müller
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