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Scopus Author ID: 56506835000

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Germany

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Employment (3)

University of Tübingen: Tübingen, DE

2022-07-01 to present | Independent Research Group Leader (Cluster of Excellence Machine Learning for Science)
Employment
Source: Self-asserted source
Nicole Ludwig

University of Tübingen: Tübingen, DE

2020-11-01 to 2022-06-30 | Early Career Research Group Leader
Employment
Source: Self-asserted source
Nicole Ludwig

Karlsruher Institut für Technologie: Karlsruhe, Baden-Württemberg, DE

2016-06-01 to 2020-10-31 | Research Assistant (Institute for Automation and Applied Informatics)
Employment
Source: Self-asserted source
Nicole Ludwig

Works (11)

Multi-Horizon Wind Power Forecasting Using Multi-Modal Spatio-Temporal Neural Networks

Energies
2023-04 | Journal article | Author
DOI: 10.3390/en16083522
Contributors: Eric Stefan Miele; Nicole Ludwig; Alessandro Corsini
Source: check_circle
Multidisciplinary Digital Publishing Institute

A comprehensive modelling framework for demand side flexibility in smart grids

Computer Science - Research and Development
2018 | Journal article
DOI: 10.1007/s00450-017-0343-x
EID:

2-s2.0-85028624478
Contributors: Barth, L.; Ludwig, N.; Mengelkamp, E.; Staudt, P.
Source: Self-asserted source
Nicole Ludwig via Scopus - Elsevier

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

e-Energy 2018 - Proceedings of the 9th ACM International Conference on Future Energy Systems
2018 | Conference paper
DOI: 10.1145/3208903.3212051
EID:

2-s2.0-85050229052
Contributors: Ludwig, N.; Waczowicz, S.; Mikut, R.; Hagenmeyer, V.
Source: Self-asserted source
Nicole Ludwig via Scopus - Elsevier

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

International Conference on the European Energy Market, EEM
2018 | Conference paper
DOI: 10.1109/EEM.2018.8470003
EID:

2-s2.0-85055558346
Contributors: Ludwig, N.; Mikut, R.; Hagenmeyer, V.
Source: Self-asserted source
Nicole Ludwig via Scopus - Elsevier

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

Journal of Big Data
2018 | Journal article
DOI: 10.1186/s40537-018-0119-6
EID:

2-s2.0-85042945857
Contributors: González Ordiano, J.Á.; Bartschat, A.; Ludwig, N.; Braun, E.; Waczowicz, S.; Renkamp, N.; Peter, N.; Düpmeier, C.; Mikut, R.; Hagenmeyer, V.
Source: Self-asserted source
Nicole Ludwig via Scopus - Elsevier

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

e-Energy 2018 - Proceedings of the 9th ACM International Conference on Future Energy Systems
2018 | Conference paper
DOI: 10.1145/3208903.3212049
EID:

2-s2.0-85050228158
Contributors: Waczowicz, S.; Ludwig, N.; Ordiano, J.Á.G.; Mikut, R.; Hagenmeyer, V.
Source: Self-asserted source
Nicole Ludwig via Scopus - Elsevier

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

e-Energy 2018 - Proceedings of the 9th ACM International Conference on Future Energy Systems
2018 | Conference paper
DOI: 10.1145/3208903.3208909
EID:

2-s2.0-85050207040
Contributors: Barth, L.; Ludwig, N.; Hagenmeyer, V.; Wagner, D.
Source: Self-asserted source
Nicole Ludwig via Scopus - Elsevier

SCiBER: A new public data set of municipal building consumption

e-Energy 2018 - Proceedings of the 9th ACM International Conference on Future Energy Systems
2018 | Conference paper
DOI: 10.1145/3208903.3210281
EID:

2-s2.0-85050190153
Contributors: Staudt, P.; Ludwig, N.; Huber, J.; Hagenmeyer, V.; Weinhardt, C.
Source: Self-asserted source
Nicole Ludwig via Scopus - Elsevier

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

GECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion
2017 | Conference paper
DOI: 10.1145/3067695.3082481
EID:

2-s2.0-85026872984
Contributors: Jakob, W.; González Ordiano, J.Á.; Ludwig, N.; Mikut, R.; Hagenmeyer, V.
Source: Self-asserted source
Nicole Ludwig via Scopus - Elsevier

Time series analysis for big data: Evaluating Bayesian structural time series using electricity prices

Multikonferenz Wirtschaftsinformatik, MKWI 2016
2016 | Conference paper
EID:

2-s2.0-84973572911
Contributors: Ludwig, N.; Feuerriegel, S.; Neumann, D.
Source: Self-asserted source
Nicole Ludwig via Scopus - Elsevier

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

Journal of Decision Systems
2015 | Journal article
DOI: 10.1080/12460125.2015.994290
EID:

2-s2.0-84924239455
Contributors: Ludwig, N.; Feuerriegel, S.; Neumann, D.
Source: Self-asserted source
Nicole Ludwig via Scopus - Elsevier

Peer review (2 reviews for 1 publication/grant)

Review activity for Applied energy (2)
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