5 citations · 20 across the 10 of their papers we have counts for
11 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…
Model Selection, Adaptation, and Combination for Transfer Learning in Wind and Photovoltaic Power Forecasts
Jens Schreiber, Bernhard Sick
There is recent interest in using model hubs, a collection of pre-trained models, in computer vision tasks. To utilize the model hub, we first select a source model and then adapt…
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…
Generative Adversarial Networks for Operational Scenario Planning of Renewable Energy Farms: A Study on Wind and Photovoltaic
Jens Schreiber, Maik Jessulat, Bernhard Sick
For the integration of renewable energy sources, power grid operators need realistic information about the effects of energy production and consumption to assess grid stability. Re…
Influences in Forecast Errors for Wind and Photovoltaic Power: A Study on Machine Learning Models
Jens Schreiber, Artjom Buschin, Bernhard Sick
Despite the increasing importance of forecasts of renewable energy, current planning studies only address a general estimate of the forecast quality to be expected and selected for…
Coopetitive Soft Gating Ensemble
Stephan Deist, Maarten Bieshaar, Jens Schreiber +2
In this article, we propose the Coopetititve Soft Gating Ensemble or CSGE for general machine learning tasks and interwoven systems. The goal of machine learning is to create model…
Quantifying the Influences on Probabilistic Wind Power Forecasts
Jens Schreiber, Bernhard Sick
In recent years, probabilistic forecasts techniques were proposed in research as well as in applications to integrate volatile renewable energy resources into the electrical grid.…