Benedikt Heidrich, M. Sc.

  • Karlsruhe Institute of Technology (KIT)
    Institute for Automation and Applied Informatics (IAI)

    Hermann-von-Helmholtz-Platz 1
    76344 Eggenstein-Leopoldshafen

    Fax: +49 721 608 22602
    Building-No.: 445 / 449 / 668

Publications


2023
Non-Sequential Machine Learning Pipelines with pyWATTS
Heidrich, B.; Phipps, K.; Meisenbacher, S.; Turowski, M.; Neumann, O.; Mikut, R.; Hagenmeyer, V.
2023. Zenodo. doi:10.5281/zenodo.7740850
2022
Towards line-restricted dispatchable feeders using probabilistic forecasts for PV-dominated low-voltage distribution grids
Werling, D.; Heidrich, B.; Çakmak, H. K.; Hagenmeyer, V.
2022. e-Energy ’22: The Thirteenth ACM International Conference on Future Energy Systems, Virtual Event, 28 June 2022- 1 July 2022. Ed.: S. Lehnhoff, 395–400, Association for Computing Machinery (ACM). doi:10.1145/3538637.3538868
Enhancing anomaly detection methods for energy time series using latent space data representations
Turowski, M.; Heidrich, B.; Phipps, K.; Schmieder, K.; Neumann, O.; Mikut, R.; Hagenmeyer, V.
2022. e-Energy ’22: The Thirteenth ACM International Conference on Future Energy Systems, Virtual Event, 28 June 2022- 1 July 2022. Ed.: S. Lehnhoff, 208–227, Association for Computing Machinery (ACM). doi:10.1145/3538637.3538851
Adaptively coping with concept drifts in energy time series forecasting using profiles
Heidrich, B.; Ludwig, N.; Turowski, M.; Mikut, R.; Hagenmeyer, V.
2022. e-Energy ’22: The Thirteenth ACM International Conference on Future Energy Systems, Virtual Event, 28 June 2022- 1 July 2022. Ed.: S. Lehnhoff, 459–470, Association for Computing Machinery (ACM). doi:10.1145/3538637.3539759
Automating Time Series Analysis Workflows with pyWATTS
Heidrich, B.; Phipps, K.; Neumann, O.; Meisenbacher, S.; Turowski, M.; Mikut, R.; Hagenmeyer, V.
2022, June. Helmholtz Artificial Intelligence Conference (Helmholtz AI 2022), Dresden, Germany, June 2–3, 2022
sktime - python toolbox for time series: pipelines and transformers
Kiraly, F.; Heidrich, B.; Parker, M.; Walter, M.
2022. pyDATA Global (2022), Online, December 1–3, 2022
Boost short-term load forecasts with synthetic data from transferred latent space information
Heidrich, B.; Mannsperger, L.; Turowski, M.; Phipps, K.; Schäfer, B.; Mikut, R.; Hagenmeyer, V.
2022. DACH+ Conference on Energy Informatics German Federal Ministry for Economic Affairs and Energy
Boost short-term load forecasts with synthetic data from transferred latent space information
Heidrich, B.; Mannsperger, L.; Turowski, M.; Phipps, K.; Schäfer, B.; Mikut, R.; Hagenmeyer, V.
2022. Energy Informatics, 5 (S1), Article no: 20. doi:10.1186/s42162-022-00214-7
Controlling non-stationarity and periodicities in time series generation using conditional invertible neural networks
Heidrich, B.; Turowski, M.; Phipps, K.; Schmieder, K.; Süß, W.; Mikut, R.; Hagenmeyer, V.
2022. Applied Intelligence. doi:10.1007/s10489-022-03742-7
Modeling and Generating Synthetic Anomalies for Energy and Power Time Series
Turowski, M.; Weber, M.; Neumann, O.; Heidrich, B.; Phipps, K.; Çakmak, H. K.; Mikut, R.; Hagenmeyer, V.
2022. e-Energy ’22: The Thirteenth ACM International Conference on Future Energy Systems, Virtual Event, 28 June 2022- 1 July 2022. Ed.: S. Lehnhoff, 471–484, Association for Computing Machinery (ACM). doi:10.1145/3538637.3539760
2021
Smart Data Representations: Impact on the Accuracy of Deep Neural Networks
Neumann, O.; Turowski, M.; Ludwig, N.; Heidrich, B.; Hagenmeyer, V.; Mikut, R.
2021. Proceedings - 31. Workshop Computational Intelligence : Berlin, 25. - 26. November 2021. Hrsg.: H. Schulte; F. Hoffmann; R. Mikut, 113–130, KIT Scientific Publishing
pyWATTS: Python Workflow Automation Tool for Time Series
Heidrich, B.; Bartschat, A.; Turowski, M.; Neumann, O.; Phipps, K.; Meisenbacher, S.; Schmieder, K.; Ludwig, N.; Mikut, R.; Hagenmeyer, V.
2021. Cornell University
2020
AI Word of the Week
Heidrich, B.
2020, July 12
Forecasting energy time series with profile neural networks
Heidrich, B.; Turowski, M.; Ludwig, N.; Mikut, R.; Hagenmeyer, V.
2020. e-Energy ’20: The Eleventh ACM International Conference on Future Energy Systems, Virtual Event, Australia, June, 2020, 220–230, Association for Computing Machinery (ACM). doi:10.1145/3396851.3397683
Coping with Concept Drifts in Load Forecasting using Machine Learning
Heidrich, B.; Turowski, M.; Ludwig, N.; Mikut, R.; Hagenmeyer, V.
2020, March 5. Helmholtz AI Kick-Off Meeting (2020), Munich, Germany, March 4–5, 2020