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Kaleb Phipps
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2020 – today
- 2024
- [b1]Kaleb Phipps:
Quantifying and Interpreting Uncertainty in Time Series Forecasting. Karlsruhe Institute of Technology, Germany, 2024 - [j6]Kaleb Phipps
, Benedikt Heidrich
Generating probabilistic forecasts from arbitrary point forecasts using a conditional invertible neural network. Appl. Intell. 54(8): 6354-6382 (2024) - [c10]Rafael Poppenborg
Dynamic Phenotype Mapping in Evolutionary Algorithms for Energy Hub Scheduling. EI.A (2) 2024: 205-223 - 2023
- [j5]Benedikt Heidrich
Controlling non-stationarity and periodicities in time series generation using conditional invertible neural networks. Appl. Intell. 53(8): 8826-8843 (2023) - [j4]Kaleb Phipps
Customized Uncertainty Quantification of Parking Duration Predictions for EV Smart Charging. IEEE Internet Things J. 10(23): 20649-20661 (2023) - [c9]Dorina Werling
The Impact of Forecast Characteristics on the Forecast Value for the Dispatchable Feeder. e-Energy (Companion) 2023: 59-71 - [c8]Kaleb Phipps
Loss-Customised Probabilistic Energy Time Series Forecasts Using Automated Hyperparameter Optimisation. e-Energy 2023: 271-286 - [c7]Dorina Werling, Maximilian Beichter, Benedikt Heidrich, Kaleb Phipps, Ralf Mikut, Veit Hagenmeyer:
Automating Value-Oriented Forecast Model Selection by Meta-learning: Application on a Dispatchable Feeder. EI.A 2023: 95-116 - [c6]Rafael Poppenborg
Dynamic Chromosome Interpretation in Evolutionary Algorithms for Distributed Energy Resources Scheduling. GECCO Companion 2023: 755-758 - [i4]Kaleb Phipps, Benedikt Heidrich, Marian Turowski, Moritz Wittig, Ralf Mikut, Veit Hagenmeyer:
Creating Probabilistic Forecasts from Arbitrary Deterministic Forecasts using Conditional Invertible Neural Networks. CoRR abs/2302.01800 (2023) - [i3]Benedikt Heidrich, Kaleb Phipps, Oliver Neumann, Marian Turowski, Ralf Mikut, Veit Hagenmeyer:
ProbPNN: Enhancing Deep Probabilistic Forecasting with Statistical Information. CoRR abs/2302.02597 (2023) - 2022
- [j3]Maximilian Beichter, Kaleb Phipps, Martha Maria Frysztacki
Net load forecasting using different aggregation levels. Energy Inform. 5 (2022) - [j2]Benedikt Heidrich, Lisa Mannsperger, Marian Turowski
Boost short-term load forecasts with synthetic data from transferred latent space information. Energy Inform. 5 (2022) - [j1]Stefan Meisenbacher
Review of automated time series forecasting pipelines. WIREs Data Mining Knowl. Discov. 12(6) (2022) - [c5]Marian Turowski
Enhancing anomaly detection methods for energy time series using latent space data representations. e-Energy 2022: 208-227 - [c4]Marian Turowski
Modeling and generating synthetic anomalies for energy and power time series. e-Energy 2022: 471-484 - [c3]Matthias Luh, Kaleb Phipps, Anthony Britto, Matthias Wolf, Marek Lutz, Johann Kraft:
High-resolution real-world electricity data from three microgrids in the global south. e-Energy 2022: 496-514 - [i2]Stefan Meisenbacher, Marian Turowski
Review of automated time series forecasting pipelines. CoRR abs/2202.01712 (2022) - 2021
- [c2]Karl Schwenk, Kaleb Phipps, Benjamin Briegel, Veit Hagenmeyer, Ralf Mikut
A Benchmark for Parking Duration Prediction of Electric Vehicles for Smart Charging Applications. SSCI 2021: 1-8 - [i1]Benedikt Heidrich, Andreas Bartschat, Marian Turowski
pyWATTS: Python Workflow Automation Tool for Time Series. CoRR abs/2106.10157 (2021)
2010 – 2019
- 2018
- [c1]Sebastian Kochanneck, Ingo Mauser, Kaleb Phipps, Hartmut Schmeck:
Hardware-in-the-Loop Co-simulation of a Smart Building in a Low-voltage Distribution Grid. ISGT Europe 2018: 1-6
Coauthor Index
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last updated on 2024-12-02 21:27 CET by the dblp team
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