| 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 | 69 | 2022 |
| Integrating battery aging in the optimization for bidirectional charging of electric vehicles K Schwenk, S Meisenbacher, B Briegel, T Harr, V Hagenmeyer, R Mikut IEEE Transactions on Smart Grid 12 (6), 5135-5145, 2021 | 65 | 2021 |
| pyWATTS: Python workflow automation tool for time series B Heidrich, A Bartschat, M Turowski, O Neumann, K Phipps, ... arXiv preprint arXiv:2106.10157, 2021 | 27 | 2021 |
| A lightweight user interface for smart charging of electric vehicles: A real-world application S Meisenbacher, K Schwenk, J Galenzowski, S Waczowicz, R Mikut, ... 2021 9th International Conference on Smart Grid and Clean Energy …, 2021 | 15 | 2021 |
| 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 | 12 | 2023 |
| Smart charging of electric vehicles with cloud-based optimization and a lightweight user interface: A real-world application in the energy lab 2.0: Poster S Meisenbacher, K Schwenk, J Galenzowski, S Waczowicz, R Mikut, ... Proceedings of the Twelfth ACM International Conference on Future Energy …, 2021 | 8 | 2021 |
| Concepts for automated machine learning in smart grid applications S Meisenbacher, J Pinter, T Martin, V Hagenmeyer, R Mikut Proceedings of the 31st Workshop on Computational Intelligence, Berlin, 25-26, 2021 | 6 | 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 | 5 | 2023 |
| Multi-zone grey-box thermal building identification with real occupants M Frahm, S Meisenbacher, E Klumpp, R Mikut, J Matthes, V Hagenmeyer Proceedings of the 9th ACM International Conference on Systems for Energy …, 2022 | 4 | 2022 |
| Development and validation of grey-box multi-zone thermal building models M Frahm, E Klumpp, S Meisenbacher, J Matthes, R Mikut, V Hagenmeyer BauSim Conference 2022 9, 0-0, 2022 | 4 | 2022 |
| 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 | 3 | 2023 |
| A Real-World District Community Platform as a Cyber-Physical-Social Infrastructure Systems in the Energy Domain J Galenzowski, S Waczowicz, S Meisenbacher, R Mikut, V Hagenmeyer Proceedings of the 10th ACM International Conference on Systems for Energy …, 2023 | 2 | 2023 |
| AutoWP: Automated wind power forecasts with limited computing resources using an ensemble of diverse wind power curves S Meisenbacher, SA Selzer, M Dado, M Beichter, T Martin, M Zdrallek, ... PROCEEDINGS 34. WORKSHOP COMPUTATIONAL INTELLIGENCE 21, 1, 2024 | 1 | 2024 |
| On autoregressive deep learning models for day-ahead wind power forecasting with irregular shutdowns due to redispatching S Meisenbacher, SA Selzer, M Dado, M Beichter, T Martin, M Zdrallek, ... arXiv preprint arXiv:2412.00423, 2024 | | 2024 |
| AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability S Meisenbacher, K Phipps, O Taubert, M Weiel, M Götz, R Mikut, ... arXiv preprint arXiv:2412.00419, 2024 | | 2024 |
| Correction to: Automation Level Taxonomy for Time Series Forecasting Services: Guideline for Real-World Smart Grid Applications S Meisenbacher, J Galenzowski, K Förderer, W Suess, S Waczowicz, ... Energy Informatics Academy Conference, C1-C2, 2024 | | 2024 |
| Automation Level Taxonomy for Time Series Forecasting Services: Guideline for Real-World Smart Grid Applications S Meisenbacher, J Galenzowski, K Förderer, W Suess, S Waczowicz, ... Energy Informatics Academy Conference, 277-297, 2024 | | 2024 |
| Non-Sequential Machine Learning Pipelines with pyWATTS B Heidrich, K Phipps, S Meisenbacher, M Turowski, O Neumann, R Mikut, ... | | 2023 |
| Comparison of methods used for forecasting time series based on air quality data. Development of a tool for predicting PM10 air pollution concentration levels. S Meisenbacher, M Turowski, K Phipps, M Rätz, D Müller, V Hagenmeyer, ... | | |