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2020 – today
- 2024
[b1]Benedikt Heidrich:
Challenges in Deep Learning Based Forecasting of Time Series with Calendar-driven Periodicities. Karlsruhe University, Germany, 2024- [j4]Kaleb Phipps
, Benedikt Heidrich, Marian Turowski, Moritz Wittig, Ralf Mikut, Veit Hagenmeyer:
Generating probabilistic forecasts from arbitrary point forecasts using a conditional invertible neural network. Appl. Intell. 54(8): 6354-6382 (2024) - [i7]Manuel Treutlein, Marc Schmidt, Roman Hahn, Matthias Hertel, Benedikt Heidrich, Ralf Mikut, Veit Hagenmeyer:
Generating peak-aware pseudo-measurements for low-voltage feeders using metadata of distribution system operators. CoRR abs/2409.19713 (2024) - 2023
- [j3]Benedikt Heidrich, Marian Turowski, Kaleb Phipps, Kai Schmieder, Wolfgang Süß, Ralf Mikut, Veit Hagenmeyer:
Controlling non-stationarity and periodicities in time series generation using conditional invertible neural networks. Appl. Intell. 53(8): 8826-8843 (2023) - [j2]Matthias Hertel, Maximilian Beichter, Benedikt Heidrich, Oliver Neumann, Benjamin Schäfer, Ralf Mikut, Veit Hagenmeyer:
Transformer training strategies for forecasting multiple load time series. Energy Inform. 6(1): 20 (2023) - [c9]Dorina Werling, Maximilian Beichter, Benedikt Heidrich, Kaleb Phipps, Ralf Mikut, Veit Hagenmeyer:
The Impact of Forecast Characteristics on the Forecast Value for the Dispatchable Feeder. e-Energy (Companion) 2023: 59-71 - [c8]Kaleb Phipps, Stefan Meisenbacher, Benedikt Heidrich, Marian Turowski, Ralf Mikut, Veit Hagenmeyer:
Loss-Customised Probabilistic Energy Time Series Forecasts Using Automated Hyperparameter Optimisation. e-Energy 2023: 271-286 - [c7]Stefan Meisenbacher, Benedikt Heidrich, Tim Martin, Ralf Mikut, Veit Hagenmeyer:
AutoPV: Automated photovoltaic forecasts with limited information using an ensemble of pre-trained models. e-Energy 2023: 386-414 - [c6]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 - [i6]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) - [i5]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) - [i4]Matthias Hertel, Maximilian Beichter, Benedikt Heidrich, Oliver Neumann, Benjamin Schäfer, Ralf Mikut, Veit Hagenmeyer:
Transformer Training Strategies for Forecasting Multiple Load Time Series. CoRR abs/2306.10891 (2023) - 2022
- [j1]Benedikt Heidrich, Lisa Mannsperger, Marian Turowski, Kaleb Phipps, Benjamin Schäfer, Ralf Mikut, Veit Hagenmeyer:
Boost short-term load forecasts with synthetic data from transferred latent space information. Energy Inform. 5 (2022) - [c5]Marian Turowski, Benedikt Heidrich, Kaleb Phipps, Kai Schmieder, Oliver Neumann, Ralf Mikut, Veit Hagenmeyer:
Enhancing anomaly detection methods for energy time series using latent space data representations. e-Energy 2022: 208-227 - [c4]Dorina Werling, Benedikt Heidrich, Hüseyin Kemâl Çakmak, Veit Hagenmeyer:
Towards line-restricted dispatchable feeders using probabilistic forecasts for PV-dominated low-voltage distribution grids. e-Energy 2022: 395-400 - [c3]Benedikt Heidrich, Nicole Ludwig, Marian Turowski, Ralf Mikut, Veit Hagenmeyer:
Adaptively coping with concept drifts in energy time series forecasting using profiles. e-Energy 2022: 459-470 - [c2]Marian Turowski, Moritz Weber, Oliver Neumann, Benedikt Heidrich, Kaleb Phipps, Hüseyin Kemâl Çakmak, Ralf Mikut, Veit Hagenmeyer:
Modeling and generating synthetic anomalies for energy and power time series. e-Energy 2022: 471-484 - [i3]Stefan Meisenbacher, Benedikt Heidrich, Tim Martin, Ralf Mikut, Veit Hagenmeyer:
AutoPV: Automated photovoltaic forecasts with limited information using an ensemble of pre-trained models. CoRR abs/2212.06797 (2022) - 2021
- [i2]Benedikt Heidrich, Andreas Bartschat, Marian Turowski, Oliver Neumann, Kaleb Phipps, Stefan Meisenbacher, Kai Schmieder, Nicole Ludwig, Ralf Mikut, Veit Hagenmeyer:
pyWATTS: Python Workflow Automation Tool for Time Series. CoRR abs/2106.10157 (2021) - [i1]Oliver Neumann, Nicole Ludwig, Marian Turowski, Benedikt Heidrich, Veit Hagenmeyer, Ralf Mikut:
Smart Data Representations: Impact on the Accuracy of Deep Neural Networks. CoRR abs/2111.09128 (2021) - 2020
- [c1]Benedikt Heidrich, Marian Turowski, Nicole Ludwig, Ralf Mikut, Veit Hagenmeyer:
Forecasting energy time series with profile neural networks. e-Energy 2020: 220-230
Coauthor Index
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