Benedikt Heidrich

Orcid: 0000-0002-1923-0848

According to our database¹, Benedikt Heidrich authored at least 19 papers between 2020 and 2024.

Collaborative distances:

Dijkstra number² of five.
Erdős number³ of four.

Timeline

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On csauthors.net:

Bibliography

2024

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

[BibT_eX]

[DOI]

Appl. Intell., April, 2024

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

[BibT_eX]

[DOI]

Benedikt Heidrich

PhD thesis, 2024

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

[BibT_eX]

[DOI]

CoRR, 2024

2023

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

[BibT_eX]

[DOI]

Appl. Intell., April, 2023

Transformer training strategies for forecasting multiple load time series.

[BibT_eX]

[DOI]

Energy Inform., January, 2023

ProbPNN: Enhancing Deep Probabilistic Forecasting with Statistical Information.

[BibT_eX]

[DOI]

CoRR, 2023

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

[BibT_eX]

[DOI]

CoRR, 2023

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

[BibT_eX]

[DOI]

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

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

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Energy Inform., 2022

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

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

CoRR, 2021

pyWATTS: Python Workflow Automation Tool for Time Series.

[BibT_eX]

[DOI]

CoRR, 2021

2020

Forecasting energy time series with profile neural networks.

[BibT_eX]

[DOI]

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

Benedikt Heidrich

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