Publikationen

Benchmarking Deep Learning Based Downscaling of Wind Speed

Luca Schmidt, Nicole Ludwig

Probabilistic Wind Speed Downscaling for Future Wind Power Assessment

Nina Effenberger, Marvin Pförtner, Philipp Hennig, Nicole Ludwig

Comparing Conditional Calibration of Aleatoric Uncertainty Estimators

Kornelius Raeth, Nicole Ludwig

Mind the (spectral) gap: How the temporal resolution of wind data affects multi-decadal wind power forecasts

Nina Effenberger, Nicole Ludwig, Rachel H. White

Analytical uncertainty propagation and storage usage in a high RES Turkish transmission grid scenario

Rebecca Bauer, Tillmann Mühlpfordt, Nicole Ludwig, Veit Hagenmeyer

Adaptively coping with concept drifts in energy time series forecasting using profiles

Benedikt Heidrich, Nicole Ludwig, Marian Turowski, Ralf Mikut, Veit Hagenmeyer

A Collection and Categorization of Open-Source Wind and Wind Power Datasets

Nina Effenberger, Nicole Ludwig

Sizing of Hybrid Energy Storage Systems Using Recurring Daily Patterns

Shahab Karrari, Nicole Ludwig, Giovanni De Carne, Mathias Noe

Evaluating ensemble post-processing for wind power forecasts

Kaleb Phipps, Sebastian Lerch, Maria Andersson, Ralf Mikut, Veit Hagenmeyer, Nicole Ludwig

ProbNum: Probabilistic Numerics in Python

Jonathan Wenger, Nicholas Krämer, Marvin Pförtner, Jonathan Schmidt, Nathanael Bosch, Nina Effenberger, Johannes Zenn, Alexandra Gessner, Toni Karvonen, François-Xavier Briol, Maren Mahsereci, Philipp Hennig

pyWATTS: Python Workflow Automation Tool for Time Series

Benedikt Heidrich, Andreas Bartschat, Marian Turowski, Oliver Neumann, Kaleb Phipps, Stefan Meisenbacher, Kai Schmieder, Nicole Ludwig, Ralf Mikut, Veit Hagenmeyer

Evaluating Ensemble Post-Processing for Wind Power Forecasts

Kaleb Phipps, Sebastian Lerch, Maria Andersson, Ralf Mikut, Veit Hagenmeyer, Nicole Ludwig

Data analytics in the electricity sector – A quantitative and qualitative literature review

Frederik vom Scheidt, Hana Medinová, Nicole Ludwig, Bent Richter, Philipp Staudt, Christof Weinhardt

Forecasting energy time series with profile neural networks

Benedikt Heidrich, Marian Turowski, Nicole Ludwig, Ralf Mikut, Veit Hagenmeyer

Potential of Ensemble Copula Coupling for Wind Power Forecasting

Kaleb Phipps, Nicole Ludwig, Veit Hagenmeyer, Ralf Mikut

Coping with Concept Drifts in Load Forecasting using Machine Learning

Benedikt Heidrich, Marian Turowski, Nicole Ludwig, Ralf Mikut, Veit Hagenmeyer

Industrial demand-side flexibility: A benchmark data set

Nicole Ludwig, Lukas Barth, Dorothea Wagner, Veit Hagenmeyer

A method for sizing centralised energy storage systems using standard patterns

Shahab Karrari, Nicole Ludwig, Veit Hagenmeyer, Mathias Noe

Auction design to use flexibility potentials in the energy - Intensive industry

Nicole Ludwig, Ralf Mikut, Veit Hagenmeyer

SCiBER: A new public data set of municipal building consumption

Philipp Staudt, Nicole Ludwig, Julian Huber, Veit Hagenmeyer, Christof Weinhardt

How much demand side flexibility do we need? Analyzing where to exploit flexibility in industrial processes

Lukas Barth, Nicole Ludwig, Veit Hagenmeyer, Dorothea Wagner

Demand response clustering — automatically finding optimal cluster hyper-parameter values

Simon Waczowicz, Nicole Ludwig, Jorge Á.G. Ordiano, Ralf Mikut, Veit Hagenmeyer

Assessment of unsupervised standard pattern recognition methods for industrial energy time series

Nicole Ludwig, Simon Waczowicz, Ralf Mikut, Veit Hagenmeyer

Concept and benchmark results for Big Data energy forecasting based on Apache Spark

Jorge Ángel González Ordiano, Andreas Bartschat, Nicole Ludwig, Eric Braun, Simon Waczowicz, Nicolas Renkamp, Nico Peter, Clemens Düpmeier, Ralf Mikut, Veit Hagenmeyer

Towards coding strategies for forecasting-based scheduling in smart grids and the energy lab 2.0

W. Jakob, J. González Ordiano, N. Ludwig, R. Mikut, V. Hagenmeyer

Mining Flexibility Patterns in Energy Time Series from Industrial Processes

Nicole Ludwig, Simon Waczowicz, Ralf Mikut, Veit Hagenmeyer

Time series analysis for big data: Evaluating Bayesian Structural Time Series using electricity prices

Nicole Ludwig, Stefan Feuerriegel, Dirk Neumann

Putting Big Data analytics to work: Feature selection for forecasting electricity prices using the LASSO and random forests

Nicole Ludwig, Stefan Feuerriegel, Dirk Neumann