Coopetitive Soft Gating Ensemble. - Jens Schreiber; Maarten Bieshaar; Andre Gensler; Bernhard Sick; Stephan Deist (FAS*W@SASO/ICAC, 2018)

Quantifying the Influences on Probabilistic Wind Power Forecasts. - Jens Schreiber; Bernhard Sick (CoRR, 2018)

Influences in Forecast Errors for Wind and Photovoltaic Power: A Study on Machine Learning Models. - Jens Schreiber; Artjom Buschin; Bernhard Sick (GI-Jahrestagung, 2019)

Generative Adversarial Networks for Operational Scenario Planning of Renewable Energy Farms: A Study on Wind and Photovoltaic. - Jens Schreiber; Maik Jessulat; Bernhard Sick (ICANN (3), 2019)

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. - Jens Schreiber (CoRR, 2019)

Emerging Relation Network and Task Embedding for Multi-Task Regression Problems. - Jens Schreiber; Bernhard Sick (ICPR, 2020)

Extended Coopetitive Soft Gating Ensemble. - Stephan Deist; Jens Schreiber; Maarten Bieshaar; Bernhard Sick (CoRR, 2020)

Quantile Surfaces - Generalizing Quantile Regression to Multivariate Targets. - Maarten Bieshaar; Jens Schreiber; Stephan Vogt; André Gensler; Bernhard Sick (CoRR, 2020)

Task Embedding Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time Series Forecast. - Jens Schreiber; Stephan Vogt; Bernhard Sick (ECML/PKDD (4), 2021)

Synthetic Photovoltaic and Wind Power Forecasting Data. - Stephan Vogt; Jens Schreiber; Bernhard Sick (CoRR, 2022)

Model Selection, Adaptation, and Combination for Deep Transfer Learning through Neural Networks in Renewable Energies. - Jens Schreiber; Bernhard Sick (CoRR, 2022)