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Jens Schreiber
Jens Schreiber
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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
172019
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
152021
Representation learning in power time series forecasting
J Henze, J Schreiber, B Sick
Deep Learning: Algorithms and Applications, 67-101, 2020
122020
Model selection, adaptation, and combination for transfer learning in wind and photovoltaic power forecasts
J Schreiber, B Sick
Energy and AI 14, 100249, 2023
102023
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
102021
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
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
82019
Synthetic Photovoltaic and Wind Power Forecasting Data
S Vogt, J Schreiber, B Sick
arXiv preprint arXiv:2204.00411, 2022
72022
Quantile Surfaces--Generalizing Quantile Regression to Multivariate Targets
M Bieshaar, J Schreiber, S Vogt, A Gensler, B Sick
arXiv preprint arXiv:2010.05898, 2020
62020
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
6*2018
Quantifying the influences on probabilistic wind power forecasts
J Schreiber, B Sick
arXiv preprint arXiv:1808.04750, 2018
62018
Multi-Task Autoencoders and Transfer Learning for Day-Ahead Wind and Photovoltaic Power Forecasts
J Schreiber, B Sick
Energies 15 (21), 8062, 2022
32022
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
32009
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, ...
22020
Extended Coopetitive Soft Gating Ensemble
S Deist, J Schreiber, M Bieshaar, B Sick
arXiv preprint arXiv:2004.14026, 2020
12020
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
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Artikelen 1–16