A review of deterministic error scores and normalization techniques for power forecasting algorithms. - Andre Gensler; Bernhard Sick; Stephan Vogt (SSCI, 2016)

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

The Problem with Real-World Novelty Detection - Issues in Multivariate Probabilistic Models. - Christian Gruhl; Abdul Hannan; Zhixin Huang; Chandana Nivarthi; Stephan Vogt (ACSOS-C, 2021)

Uncertainty and Utility Sampling with Pre-Clustering. - Zhixin Huang; Yujiang He; Stephan Vogt; Bernhard Sick (IAL@PKDD/ECML, 2021)

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)

Unified Autoencoder with Task Embeddings for Multi-Task Learning in Renewable Power Forecasting. - Chandana Priya Nivarthi; Stephan Vogt; Bernhard Sick (ICMLA, 2022)

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

Fast Bayesian Updates for Deep Learning with a Use Case in Active Learning. - Marek Herde; Zhixin Huang; Denis Huseljic; Daniel Kottke; Stephan Vogt; Bernhard Sick (CoRR, 2022)

Multi-Task Representation Learning for Renewable-Power Forecasting: A Comparative Analysis of Unified Autoencoder Variants and Task-Embedding Dimensions. - Chandana Priya Nivarthi; Stephan Vogt; Bernhard Sick (Mach. Learn. Knowl. Extr., 2023)

Targeted adversarial attacks on wind power forecasts. - René Heinrich; Christoph Scholz 001; Stephan Vogt; Malte Lehna (Mach. Learn., 2024)

Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios. - Ben Gerhards; Nikita Popkov; Annekatrin König; Marcel Arpogaus; Bastian Schäfermeier; Leonie Riedl; Stephan Vogt; Philip Hehlert (CoRR, 2025)