Nicole Ludwig
Cited by
Cited by
Putting Big Data analytics to work: Feature selection for forecasting electricity prices using the LASSO and random forests
N Ludwig, S Feuerriegel, D Neumann
Journal of Decision Systems 24 (1), 19-36, 2015
Data analytics in the electricity sector–A quantitative and qualitative literature review
F vom Scheidt, H Medinová, N Ludwig, B Richter, P Staudt, C Weinhardt
Energy and AI 1, 100009, 2020
A comprehensive modelling framework for demand side flexibility in smart grids
L Barth, N Ludwig, E Mengelkamp, P Staudt
Computer Science-Research and Development 33 (1), 13-23, 2018
pyWATTS: Python workflow automation tool for time series
B Heidrich, A Bartschat, M Turowski, O Neumann, K Phipps, ...
arXiv preprint arXiv:2106.10157, 2021
Mining flexibility patterns in energy time series from industrial processes
N Ludwig, S Waczowicz, R Mikut, V Hagenmeyer, F Hoffmann, ...
Proceedings. 27. Workshop Computational Intelligence Dortmund, 23. - 24 …, 2017
How much demand side flexibility do we need? Analyzing where to exploit flexibility in industrial processes
L Barth, V Hagenmeyer, N Ludwig, D Wagner
Proceedings of the Ninth International Conference on Future Energy Systems …, 2018
Concept and benchmark results for Big Data energy forecasting based on Apache Spark
JÁ González Ordiano, A Bartschat, N Ludwig, E Braun, S Waczowicz, ...
Journal of Big Data 5, 1-11, 2018
Forecasting energy time series with profile neural networks
B Heidrich, M Turowski, N Ludwig, R Mikut, V Hagenmeyer
Proceedings of the eleventh acm international conference on future energy …, 2020
Evaluating ensemble post‐processing for wind power forecasts
K Phipps, S Lerch, M Andersson, R Mikut, V Hagenmeyer, N Ludwig
Wind Energy 25 (8), 1379-1405, 2022
Industrial demand-side flexibility: A benchmark data set
N Ludwig, L Barth, D Wagner, V Hagenmeyer
Proceedings of the Tenth ACM International Conference on Future Energy …, 2019
A collection and categorization of open‐source wind and wind power datasets
N Effenberger, N Ludwig
Wind Energy 25 (10), 1659-1683, 2022
Towards coding strategies for forecasting-based scheduling in smart grids and the energy lab 2.0
W Jakob, JÁG Ordiano, N Ludwig, R Mikut, V Hagenmeyer
Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2017
Sizing of hybrid energy storage systems using recurring daily patterns
S Karrari, N Ludwig, G De Carne, M Noe
IEEE Transactions on Smart Grid 13 (4), 3290-3300, 2022
Sciber: A new public data set of municipal building consumption
P Staudt, N Ludwig, J Huber, V Hagenmeyer, C Weinhardt
Proceedings of the Ninth International Conference on Future Energy Systems …, 2018
Net load forecasting using different aggregation levels
M Beichter, K Phipps, MM Frysztacki, R Mikut, V Hagenmeyer, N Ludwig
Energy Informatics 5 (Suppl 1), 19, 2022
Analytical uncertainty propagation for multi-period stochastic optimal power flow
R Bauer, T Mühlpfordt, N Ludwig, V Hagenmeyer
Sustainable Energy, Grids and Networks 33, 100969, 2023
A method for sizing centralised energy storage systems using standard patterns
S Karrari, N Ludwig, V Hagenmeyer, M Noe
2019 IEEE Milan PowerTech, 1-6, 2019
Probabilistic load forecasting using post-processed weather ensemble predictions
N Ludwig, S Arora, JW Taylor
Journal of the Operational Research Society, 2023
Multi-Horizon Wind Power Forecasting Using Multi-Modal Spatio-Temporal Neural Networks
ES Miele, N Ludwig, A Corsini
Energies 16 (8), 3522, 2023
Data-Driven Methods for Demand-Side Flexibility in Energy Systems
NN Ludwig
Dissertation, Karlsruhe, Karlsruher Institut für Technologie (KIT), 2020, 2020
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