5 citations · 16 across the 7 of their papers we have counts for
9 papers
Task Embedding Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time-Series Forecast
Jens Schreiber, Stephan Vogt, Bernhard Sick
Task embeddings in multi-layer perceptrons for multi-task learning and inductive transfer learning in renewable power forecasts have recently been introduced. In many cases, this a…
Synthetic Photovoltaic and Wind Power Forecasting Data
Stephan Vogt, Jens Schreiber, Bernhard Sick
Photovoltaic and wind power forecasts in power systems with a high share of renewable energy are essential in several applications. These include stable grid operation, profitable…
Quantile Surfaces -- Generalizing Quantile Regression to Multivariate Targets
Maarten Bieshaar, Jens Schreiber, Stephan Vogt +2
In this article, we present a novel approach to multivariate probabilistic forecasting. Our approach is based on an extension of single-output quantile regression (QR) to multivari…
Emerging Relation Network and Task Embedding for Multi-Task Regression Problems
Jens Schreiber, Bernhard Sick
Multi-task learning (mtl) provides state-of-the-art results in many applications of computer vision and natural language processing. In contrast to single-task learning (stl), mtl…
Extended Coopetitive Soft Gating Ensemble
Stephan Deist, Jens Schreiber, Maarten Bieshaar +1
This article is about an extension of a recent ensemble method called Coopetitive Soft Gating Ensemble (CSGE) and its application on power forecasting as well as motion primitive f…
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
In recent years, transfer learning gained particular interest in the field of vision and natural language processing. In the research field of vision, e.g., deep neural networks an…