Benedikt Heidrich

According to our database, Benedikt Heidrich authored at least 19 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Generating probabilistic forecasts from arbitrary point forecasts using a conditional invertible neural network.

Kaleb Phipps, Benedikt Heidrich, Marian Turowski, Moritz Wittig, Ralf Mikut, Veit Hagenmeyer

Appl. Intell., April, 2024

Challenges in Deep Learning Based Forecasting of Time Series with Calendar-driven Periodicities

Benedikt Heidrich

PhD thesis, 2024

Generating peak-aware pseudo-measurements for low-voltage feeders using metadata of distribution system operators.

Manuel Treutlein, Marc Schmidt, Roman Hahn, Matthias Hertel, Benedikt Heidrich, Ralf Mikut, Veit Hagenmeyer

CoRR, 2024

2023

Controlling non-stationarity and periodicities in time series generation using conditional invertible neural networks.

Benedikt Heidrich, Marian Turowski, Kaleb Phipps, Kai Schmieder, Wolfgang Süß, Ralf Mikut, Veit Hagenmeyer

Appl. Intell., April, 2023

Transformer training strategies for forecasting multiple load time series.

Matthias Hertel, Maximilian Beichter, Benedikt Heidrich, Oliver Neumann, Benjamin Schäfer, Ralf Mikut, Veit Hagenmeyer

Energy Inform., January, 2023

ProbPNN: Enhancing Deep Probabilistic Forecasting with Statistical Information.

Benedikt Heidrich, Kaleb Phipps, Oliver Neumann, Marian Turowski, Ralf Mikut, Veit Hagenmeyer

CoRR, 2023

Creating Probabilistic Forecasts from Arbitrary Deterministic Forecasts using Conditional Invertible Neural Networks.

Kaleb Phipps, Benedikt Heidrich, Marian Turowski, Moritz Wittig, Ralf Mikut, Veit Hagenmeyer

CoRR, 2023

Automating Value-Oriented Forecast Model Selection by Meta-learning: Application on a Dispatchable Feeder.

Dorina Werling, Maximilian Beichter, Benedikt Heidrich, Kaleb Phipps, Ralf Mikut, Veit Hagenmeyer

Proceedings of the Energy Informatics - Third Energy Informatics Academy Conference, 2023

The Impact of Forecast Characteristics on the Forecast Value for the Dispatchable Feeder.

Dorina Werling, Maximilian Beichter, Benedikt Heidrich, Kaleb Phipps, Ralf Mikut, Veit Hagenmeyer

Proceedings of the Companion Proceedings of the 14th ACM International Conference on Future Energy Systems, 2023

Loss-Customised Probabilistic Energy Time Series Forecasts Using Automated Hyperparameter Optimisation.

Kaleb Phipps, Stefan Meisenbacher, Benedikt Heidrich, Marian Turowski, Ralf Mikut, Veit Hagenmeyer

Proceedings of the 14th ACM International Conference on Future Energy Systems, 2023

AutoPV: Automated photovoltaic forecasts with limited information using an ensemble of pre-trained models.

Stefan Meisenbacher, Benedikt Heidrich, Tim Martin, Ralf Mikut, Veit Hagenmeyer

Proceedings of the 14th ACM International Conference on Future Energy Systems, 2023

2022

Boost short-term load forecasts with synthetic data from transferred latent space information.

Benedikt Heidrich, Lisa Mannsperger, Marian Turowski, Kaleb Phipps, Benjamin Schäfer, Ralf Mikut, Veit Hagenmeyer

Energy Inform., 2022

Towards line-restricted dispatchable feeders using probabilistic forecasts for PV-dominated low-voltage distribution grids.

Dorina Werling, Benedikt Heidrich, Hüseyin Kemâl Çakmak, Veit Hagenmeyer

Proceedings of the e-Energy '22: The Thirteenth ACM International Conference on Future Energy Systems, Virtual Event, 28 June 2022, 2022

Modeling and generating synthetic anomalies for energy and power time series.

Marian Turowski, Moritz Weber, Oliver Neumann, Benedikt Heidrich, Kaleb Phipps, Hüseyin Kemâl Çakmak, Ralf Mikut, Veit Hagenmeyer

Proceedings of the e-Energy '22: The Thirteenth ACM International Conference on Future Energy Systems, Virtual Event, 28 June 2022, 2022

Enhancing anomaly detection methods for energy time series using latent space data representations.

Marian Turowski, Benedikt Heidrich, Kaleb Phipps, Kai Schmieder, Oliver Neumann, Ralf Mikut, Veit Hagenmeyer

Proceedings of the e-Energy '22: The Thirteenth ACM International Conference on Future Energy Systems, Virtual Event, 28 June 2022, 2022

Adaptively coping with concept drifts in energy time series forecasting using profiles.

Benedikt Heidrich, Nicole Ludwig, Marian Turowski, Ralf Mikut, Veit Hagenmeyer

Proceedings of the e-Energy '22: The Thirteenth ACM International Conference on Future Energy Systems, Virtual Event, 28 June 2022, 2022

2021

Smart Data Representations: Impact on the Accuracy of Deep Neural Networks.

Oliver Neumann, Nicole Ludwig, Marian Turowski, Benedikt Heidrich, Veit Hagenmeyer, Ralf Mikut

CoRR, 2021

pyWATTS: Python Workflow Automation Tool for Time Series.

Benedikt Heidrich, Andreas Bartschat, Marian Turowski, Oliver Neumann, Kaleb Phipps, Stefan Meisenbacher, Kai Schmieder, Nicole Ludwig, Ralf Mikut, Veit Hagenmeyer

CoRR, 2021

2020

Forecasting energy time series with profile neural networks.

Benedikt Heidrich, Marian Turowski, Nicole Ludwig, Ralf Mikut, Veit Hagenmeyer

Proceedings of the e-Energy '20: The Eleventh ACM International Conference on Future Energy Systems, 2020