| Review of automated time series forecasting pipelines S Meisenbacher, M Turowski, K Phipps, M Rätz, D Müller, V Hagenmeyer, ... Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 12 (6 …, 2022 | 66 | 2022 |
| Model diagnostics and forecast evaluation for quantiles T Gneiting, D Wolffram, J Resin, K Kraus, J Bracher, T Dimitriadis, ... Annual Review of Statistics and Its Application 10 (1), 597-621, 2023 | 29 | 2023 |
| pyWATTS: Python workflow automation tool for time series B Heidrich, A Bartschat, M Turowski, O Neumann, K Phipps, ... arXiv preprint arXiv:2106.10157, 2021 | 28 | 2021 |
| Evaluating ensemble post-processing for wind power forecasts K Phipps, S Lerch, M Andersson, R Mikut, V Hagenmeyer, N Ludwig Wind Energy, 2022 | 25 | 2022 |
| 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 | 18 | 2023 |
| Net load forecasting using different aggregation levels M Beichter, K Phipps, MM Frysztacki, R Mikut, V Hagenmeyer, N Ludwig Energy Informatics 5 (Suppl 1), 19, 2022 | 18 | 2022 |
| Hardware-in-the-Loop Co-simulation of a Smart Building in a Low-voltage Distribution Grid S Kochanneck, I Mauser, K Phipps, H Schmeck 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT …, 2018 | 17 | 2018 |
| 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 | 16 | 2022 |
| A benchmark for parking duration prediction of electric vehicles for smart charging applications K Schwenk, K Phipps, B Briegel, V Hagenmeyer, R Mikut 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 1-8, 2021 | 11 | 2021 |
| Customized uncertainty quantification of parking duration predictions for EV smart charging K Phipps, K Schwenk, B Briegel, R Mikut, V Hagenmeyer IEEE Internet of Things Journal 10 (23), 20649-20661, 2023 | 9 | 2023 |
| 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 | 8 | 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 | 8 | 2022 |
| 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 | 6 | 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 | 6 | 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 | 6 | 2022 |
| Potential of ensemble copula coupling for wind power forecasting K Phipps, N Ludwig, V Hagenmeyer, R Mikut Proceedings 30. Workshop Computational Intelligence 26, 87, 2020 | 5 | 2020 |
| Generating synthetic energy time series: A review M Turowski, B Heidrich, L Weingärtner, L Springer, K Phipps, B Schäfer, ... Renewable and Sustainable Energy Reviews 206, 114842, 2024 | 4 | 2024 |
| 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 | 4 | 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 54 (8), 6354-6382, 2024 | 3 | 2024 |
| 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 |