Nicole Ludwig

Orcid: 0000-0003-3230-8918

Affiliations:
  • University of Tübingen, Germany
  • Karlsruhe Institute of Technology (KIT), Germany


According to our database1, Nicole Ludwig authored at least 23 papers between 2015 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2023
Probabilistic load forecasting using post-processed weather ensemble predictions.
J. Oper. Res. Soc., March, 2023

Using weather data in energy time series forecasting: the benefit of input data transformations.
Energy Inform., January, 2023

Inductive biases in deep learning models for weather prediction.
CoRR, 2023

2022
Sizing of Hybrid Energy Storage Systems Using Recurring Daily Patterns.
IEEE Trans. Smart Grid, 2022

Net load forecasting using different aggregation levels.
Energy Inform., 2022

Analytical Uncertainty Propagation for Multi-Period Stochastic Optimal Power Flow.
CoRR, 2022

A Collection and Categorization of Open-Source Wind and Wind Power Datasets.
CoRR, 2022

Adaptively coping with concept drifts in energy time series forecasting using profiles.
Proceedings of the e-Energy '22: The Thirteenth ACM International Conference on Future Energy Systems, Virtual Event, 28 June 2022, 2022

Analytical uncertainty propagation and storage usage in a high RES Turkish transmission grid scenario.
Proceedings of the e-Energy '22: The Thirteenth ACM International Conference on Future Energy Systems, Virtual Event, 28 June 2022, 2022

2021
Smart Data Representations: Impact on the Accuracy of Deep Neural Networks.
CoRR, 2021

pyWATTS: Python Workflow Automation Tool for Time Series.
CoRR, 2021

KIU-2021 Keynote: Uncertainty in Machine Learning for Environmental Research.
Proceedings of the 51. Jahrestagung der Gesellschaft für Informatik, INFORMATIK 2021 - Computer Science & Sustainability, Berlin, Germany, 27. September, 2021

2020
Data-Driven Methods for Demand-Side Flexibility in Energy Systems
PhD thesis, 2020

Forecasting energy time series with profile neural networks.
Proceedings of the e-Energy '20: The Eleventh ACM International Conference on Future Energy Systems, 2020

2019
Industrial Demand-Side Flexibility: A Benchmark Data Set.
Proceedings of the Tenth ACM International Conference on Future Energy Systems, 2019

2018
Concept and benchmark results for Big Data energy forecasting based on Apache Spark.
J. Big Data, 2018

A comprehensive modelling framework for demand side flexibility in smart grids.
Comput. Sci. Res. Dev., 2018

Demand Response clustering: Automatically finding optimal cluster hyper-parameter values.
Proceedings of the Ninth International Conference on Future Energy Systems, 2018

SCiBER: A new public data set of municipal building consumption.
Proceedings of the Ninth International Conference on Future Energy Systems, 2018

How much demand side flexibility do we need?: Analyzing where to exploit flexibility in industrial processes.
Proceedings of the Ninth International Conference on Future Energy Systems, 2018

Assessment of Unsupervised Standard Pattern Recognition Methods for Industrial Energy Time Series.
Proceedings of the Ninth International Conference on Future Energy Systems, 2018

2017
Towards coding strategies for forecasting-based scheduling in smart grids and the energy lab 2.0.
Proceedings of the Genetic and Evolutionary Computation Conference, 2017

2015
Putting Big Data analytics to work: Feature selection for forecasting electricity prices using the LASSO and random forests.
J. Decis. Syst., 2015


  Loading...