| pyWATTS: Python workflow automation tool for time series B Heidrich, A Bartschat, M Turowski, O Neumann, K Phipps, ... arXiv preprint arXiv:2106.10157, 2021 | 24 | 2021 |
| Forecasting energy time series with profile neural networks B Heidrich, M Turowski, N Ludwig, R Mikut, V Hagenmeyer Proceedings of the eleventh acm international conference on future energy …, 2020 | 19 | 2020 |
| Controlling non-stationarity and periodicities in time series generation using conditional invertible neural networks B Heidrich, M Turowski, K Phipps, K Schmieder, W Süß, R Mikut, ... Applied Intelligence 53 (8), 8826-8843, 2023 | 13 | 2023 |
| Modeling and generating synthetic anomalies for energy and power time series M Turowski, M Weber, O Neumann, B Heidrich, K Phipps, HK Çakmak, ... Proceedings of the Thirteenth ACM International Conference on Future Energy …, 2022 | 10 | 2022 |
| Transformer training strategies for forecasting multiple load time series M Hertel, M Beichter, B Heidrich, O Neumann, B Schäfer, R Mikut, ... Energy Informatics 6 (Suppl 1), 20, 2023 | 7 | 2023 |
| Towards line-restricted dispatchable feeders using probabilistic forecasts for PV-dominated low-voltage distribution grids D Werling, B Heidrich, HK Çakmak, V Hagenmeyer Proceedings of the Thirteenth ACM International Conference on Future Energy …, 2022 | 7 | 2022 |
| AutoPV: Automated photovoltaic forecasts with limited information using an ensemble of pre-trained models S Meisenbacher, B Heidrich, T Martin, R Mikut, V Hagenmeyer Proceedings of the 14th ACM International Conference on Future Energy …, 2023 | 6 | 2023 |
| Enhancing anomaly detection methods for energy time series using latent space data representations M Turowski, B Heidrich, K Phipps, K Schmieder, O Neumann, R Mikut, ... Proceedings of the Thirteenth ACM International Conference on Future Energy …, 2022 | 6 | 2022 |
| The impact of forecast characteristics on the forecast value for the dispatchable feeder D Werling, M Beichter, B Heidrich, K Phipps, R Mikut, V Hagenmeyer Companion Proceedings of the 14th ACM International Conference on Future …, 2023 | 5 | 2023 |
| ProbPNN: Enhancing Deep Probabilistic Forecasting with Statistical Information B Heidrich, K Phipps, O Neumann, M Turowski, R Mikut, V Hagenmeyer arXiv preprint arXiv:2302.02597, 2023 | 5 | 2023 |
| Boost short-term load forecasts with synthetic data from transferred latent space information B Heidrich, L Mannsperger, M Turowski, K Phipps, B Schäfer, R Mikut, ... Energy Informatics 5 (Suppl 1), 20, 2022 | 5 | 2022 |
| Automating Value-Oriented Forecast Model Selection by Meta-learning: Application on a Dispatchable Feeder D Werling, M Beichter, B Heidrich, K Phipps, R Mikut, V Hagenmeyer Energy Informatics Academy Conference, 95-116, 2023 | 3 | 2023 |
| Creating probabilistic forecasts from arbitrary deterministic forecasts using conditional invertible neural networks K Phipps, B Heidrich, M Turowski, M Wittig, R Mikut, V Hagenmeyer arXiv preprint arXiv:2302.01800, 2023 | 3 | 2023 |
| Adaptively coping with concept drifts in energy time series forecasting using profiles B Heidrich, N Ludwig, M Turowski, R Mikut, V Hagenmeyer Proceedings of the Thirteenth ACM International Conference on Future Energy …, 2022 | 3 | 2022 |
| Smart data representations: impact on the accuracy of deep neural networks O Neumann, N Ludwig, M Turowski, B Heidrich, V Hagenmeyer, R Mikut Proceedings 31 workshop computational intelligence, 113-130, 2021 | 3 | 2021 |
| Loss-Customised Probabilistic Energy Time Series Forecasts Using Automated Hyperparameter Optimisation K Phipps, S Meisenbacher, B Heidrich, M Turowski, R Mikut, ... Proceedings of the 14th ACM International Conference on Future Energy …, 2023 | 2 | 2023 |
| Generating probabilistic forecasts from arbitrary point forecasts using a conditional invertible neural network K Phipps, B Heidrich, M Turowski, M Wittig, R Mikut, V Hagenmeyer Applied Intelligence, 1-29, 2024 | 1 | 2024 |
| Using conditional Invertible Neural Networks to perform mid‐term peak load forecasting B Heidrich, M Hertel, O Neumann, V Hagenmeyer, R Mikut IET Smart Grid, 2024 | 1 | 2024 |
| Automating day-ahead forecasting of photovoltaic power generation: Model design, monitoring, and adaption S Meisenbacher, T Martin, B Heidrich, R Mikut, V Hagenmeyer ETG Congress 2023, 1-8, 2023 | 1 | 2023 |
| Delay-robust Estimation of the Reproduction Number and Comparative Evaluation on Generated Synthetic Data B Heidrich, T Mühlpfordt, V Hagenmeyer, R Mikut medRxiv, 2020.11. 27.20238618, 2020 | 1 | 2020 |
Veit HagenmeyerKITVerified email at kit.edu
