Kaleb Phipps

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

Publikationen


2022
High-resolution real-world electricity data from three microgrids in the global south
Luh, M.; Phipps, K.; Britto, A.; Wolf, M.; Lutz, M.; Kraft, J.
2022. e-Energy ’22: Proceedings of the Thirteenth ACM International Conference on Future Energy Systems, 496–514, Association for Computing Machinery (ACM). doi:10.1145/3538637.3539763
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
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
High-Resolution Real-World Electricity Data from Three Microgrids in the Global South
Luh, M.; Phipps, K.; Britto, A.; Wolf, M.; Lutz, M.; Kraft, J.
2022, Mai 23. doi:10.5445/IR/1000143466
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
Evaluating ensemble post‐processing for wind power forecasts
Phipps, K.; Lerch, S.; Andersson, M.; Mikut, R.; Hagenmeyer, V.; Ludwig, N.
2022. Wind Energy. doi:10.1002/we.2736
High-Resolution Real-World Electricity Data from Three Microgrids in the Global South
Luh, M.; Phipps, K.; Britto, A.; Wolf, M.; Lutz, M.; Kraft, J.
2022. ACM Digital Library. doi:10.5445/IR/1000144567
2021
A Benchmark for Parking Duration Prediction of Electric Vehicles for Smart Charging Applications
Schwenk, K.; Phipps, K.; Briegel, B.; Hagenmeyer, V.; Mikut, R.
2021. 2021 IEEE Symposium Series on Computational Intelligence (SSCI), Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/SSCI50451.2021.9660063
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
Potential of Ensemble Copula Coupling for Wind Power Forecasting
Phipps, K.; Ludwig, N.; Hagenmeyer, V.; Mikut, R.
2020. Proceedings - 30. Workshop Computational Intelligence : Berlin, 26. - 27. November 2020, 87–109, KIT Scientific Publishing