5 citations · 12 across the 6 of their papers we have counts for
6 papers
Time-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios
Ben Gerhards, Nikita Popkov, Annekatrin König +5
Forecasting attracts a lot of research attention in the electricity value chain. However, most studies concentrate on short-term forecasting of generation or consumption with a foc…
Targeted Adversarial Attacks on Wind Power Forecasts
René Heinrich, Christoph Scholz, Stephan Vogt +1
In recent years, researchers proposed a variety of deep learning models for wind power forecasting. These models predict the wind power generation of wind farms or entire regions m…
Efficient Bayesian Updates for Deep Active Learning via Laplace Approximations
Denis Huseljic, Marek Herde, Lukas Rauch +5
Deep active learning (AL) selects batches of instances for annotation to avoid retraining deep neural networks (DNNs) after each new label. Employing a naive top- selection can…
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…