Jens Schreiber

Model selection, adaptation, and combination for transfer learning in wind and photovoltaic power forecasts

B. Sick, Jens Schreiber, 2022, Energy and AI.

Model Selection, Adaptation, and Combination for Deep Transfer Learning through Neural Networks in Renewable Energies

B. Sick, Jens Schreiber, 2022, ArXiv.

Quantile Surfaces - Generalizing Quantile Regression to Multivariate Targets pdf

Bernhard Sick, Maarten Bieshaar, Stephan Vogt, 2020, ArXiv.

Representation Learning in Power Time Series Forecasting

Bernhard Sick, Janosch Henze, Jens Schreiber, 2019, Deep Learning: Algorithms and Applications.

Quantifying the Influences on Probabilistic Wind Power Forecasts pdf

Bernhard Sick, Jens Schreiber, B. Sick, 2018, ArXiv.

Localized Argyria of the Proximal Trachea

J. Knolle, Jens Schreiber, C. Schreiber, 2005 .

Transfer Learning in the Field of Renewable Energies - A Transfer Learning Framework Providing Power Forecasts Throughout the Lifecycle of Wind Farms After Initial Connection to the Electrical Grid pdf

Jens Schreiber, Jens Schreiber, 2019, ArXiv.

Influences in Forecast Errors for Wind and Photovoltaic Power: A Study on Machine Learning Models

Bernhard Sick, Jens Schreiber, Artjom Buschin, 2019, GI-Jahrestagung.

Coopetitive Soft Gating Ensemble

Bernhard Sick, Maarten Bieshaar, Andre Gensler, 2018, 2018 IEEE 3rd International Workshops on Foundations and Applications of Self* Systems (FAS*W).