Marian Turowski, M.Sc.

  • Karlsruher Institut für Technologie (KIT)
    Institut für Automation und angewandte Informatik (IAI)

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

    Fax: +49 721 608 22602
    Gebäude-Nr.: 445 / 449 / 668

Publikationen


2022
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, Juni. Helmholtz Artificial Intelligence Conference (Helmholtz AI 2022), Dresden, Deutschland, 2.–3. Juni 2022
ALDI++: Automatic and parameter-less discord and outlier detection for building energy load profiles
Quintana, M.; Stoeckmann, T.; Park, J. Y.; Turowski, M.; Hagenmeyer, V.; Miller, C.
2022. Energy and Buildings, 265, Art.-Nr.: 112096. doi:10.1016/j.enbuild.2022.112096
Review of automated time series forecasting pipelines
Meisenbacher, S.; Turowski, M.; Phipps, K.; Rätz, M.; Hagenmeyer, V.; Müller, D.; Mikut, R.
2022. Karlsruher Institut für Technologie (KIT). doi:10.48550/arXiv.2202.01712
Review of automated time series forecasting pipelines
Meisenbacher, S.; Turowski, M.; Phipps, K.; Rätz, M.; Müller, D.; Hagenmeyer, V.; Mikut, R.
2022. WIREs Data Mining and Knowledge Discovery. doi:10.1002/widm.1475
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
Data-Driven Copy-Paste Imputation for Energy Time Series
Weber, M.; Turowski, M.; Çakmak, H. K.; Mikut, R.; Kühnapfel, U.; Hagenmeyer, V.
2021. IEEE transactions on smart grid, 12 (6), 5409–5419. doi:10.1109/TSG.2021.3101831
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
Point and contextual anomaly detection in building load profiles of a university campus
Wang, L.; Turowski, M.; Zhang, M.; Riedel, T.; Beigl, M.; Mikut, R.; Hagenmeyer, V.
2020. 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), 11–15, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ISGT-Europe47291.2020.9248792
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, März 5. Helmholtz AI Kick-Off Meeting (2020), München, Deutschland, 4.–5. März 2020