Publikationen

Model Diagnostics and Forecast Evaluation for Quantiles
Gneiting, T.; Wolffram, D.; Resin, J.; Kraus, K.; Bracher, J.; Dimitriadis, T.; Hagenmeyer, V.; Jordan, A. I.; Lerch, S.; Phipps, K.; Schienle, M.
2023. Annual Review of Statistics and Its Application, 10. doi:10.1146/annurev-statistics-032921-020240

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

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, 12 (6), Art.Nr. e1475. doi:10.1002/widm.1475

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

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

Net load forecasting using different aggregation levels
Beichter, M.; Phipps, K.; Frysztacki, M. M.; Mikut, R.; Hagenmeyer, V.; Ludwig, N.
2022. Energy Informatics, 5 (S1), Article no: 19. doi:10.1186/s42162-022-00213-8

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, 25 (8), 1379–1405. 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

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

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

Hardware-in-The-Loop Co-simulation of a Smart Building in a Low-voltage Distribution Grid
Kochanneck, S.; Mauser, I.; Phipps, K.; Schmeck, H.
2018. 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2018; Sarajevo; Bosnia and Herzegovina; 21 October 2018 through 25 October 2018, Art. Nr.: 8571746, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ISGTEurope.2018.8571746

Hardware-in-the-Loop Co-simulation of a SmartBuilding in a Low-voltage Distribution Grid [in press]
Kochanneck, S.; Mauser, I.; Phipps, K.; Schmeck, H.
2018. 8th IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), Sarajevo, Bosnia and Herzegovina, October 21 - 25, 2018