| Generative adversarial networks for operational scenario planning of renewable energy farms: a study on wind and photovoltaic J Schreiber, M Jessulat, B Sick Artificial Neural Networks and Machine Learning–ICANN 2019: Image Processing …, 2019 | 19 | 2019 |
| Emerging relation network and task embedding for multi-task regression problems J Schreiber, B Sick 2020 25th International Conference on Pattern Recognition (ICPR), 2663-2670, 2021 | 17 | 2021 |
| Model selection, adaptation, and combination for transfer learning in wind and photovoltaic power forecasts J Schreiber, B Sick Energy and AI 14, 100249, 2023 | 14 | 2023 |
| Representation learning in power time series forecasting J Henze, J Schreiber, B Sick Deep Learning: Algorithms and Applications, 67-101, 2020 | 14 | 2020 |
| Task embedding temporal convolution networks for transfer learning problems in renewable power time series forecast J Schreiber, S Vogt, B Sick Joint European Conference on Machine Learning and Knowledge Discovery in …, 2021 | 10 | 2021 |
| A Transfer Learning Framework Providing Power Forecasts Throughout the Lifecycle of Wind Farms After Initial Connection to the Electrical Grid J Schreiber Organic Computing: Doctoral Dissertation Colloquium 2018 13, 75, 2019 | 10* | 2019 |
| Quantile Surfaces--Generalizing Quantile Regression to Multivariate Targets M Bieshaar, J Schreiber, S Vogt, A Gensler, B Sick arXiv preprint arXiv:2010.05898, 2020 | 8 | 2020 |
| Influences in forecast errors for wind and photovoltaic power: a study on machine learning models J Schreiber, A Buschin, B Sick arXiv preprint arXiv:1905.13668, 2019 | 8 | 2019 |
| Synthetic Photovoltaic and Wind Power Forecasting Data S Vogt, J Schreiber, B Sick arXiv preprint arXiv:2204.00411, 2022 | 7 | 2022 |
| Coopetitive soft gating ensemble J Schreiber, M Bieshaar, A Gensler, B Sick, S Deist 2018 IEEE 3rd International Workshops on Foundations and Applications of …, 2018 | 7* | 2018 |
| Quantifying the influences on probabilistic wind power forecasts J Schreiber, B Sick arXiv preprint arXiv:1808.04750, 2018 | 6 | 2018 |
| Multi-Task Autoencoders and Transfer Learning for Day-Ahead Wind and Photovoltaic Power Forecasts J Schreiber, B Sick Energies 15 (21), 8062, 2022 | 4 | 2022 |
| Carpe noctem 2009 T Amma, P Baer, K Baumgart, P Burghardt, K Geihs, J Henze, S Opfer, ... RoboCup 2009 International Symposium. TU Graz, Graz, 2009 | 3 | 2009 |
| Abschlussbericht Projekt Prophesy-Prognoseunsicherheiten von Windenergie und Photovoltaik in zukünftigen Stromversorgungssystemen J Schreiber, M Siefert, K Winter, A Wessel, R Fritz, G Good, A Schella, ... | 2 | 2020 |
| Extended Coopetitive Soft Gating Ensemble S Deist, J Schreiber, M Bieshaar, B Sick arXiv preprint arXiv:2004.14026, 2020 | 1 | 2020 |
| TRANSFER-Transfer Learning als essentielles Werkzeug für die Energiewende. Sachbericht D Beinert, K Brauns, G Hein, RPG Heinrich, DE Hollermann, M Jürgens, ... Fraunhofer IEE, 2023 | | 2023 |
Bernhard SickProfessor of Intelligent Embedded Systems, University of KasselVerified email at uni-kassel.de
