Publications

Decision-focused fine-tuning of time series foundation models for dispatchable feeder optimization
Beichter, M.; Friederich, N.; Pinter, J.; Werling, D.; Phipps, K.; Beichter, S.; Neumann, O.; Mikut, R.; Hagenmeyer, V.; Heidrich, B.
2025. Energy and AI, 21, 100533. doi:10.1016/j.egyai.2025.100533

BEOP: A Framework Enabling Validation of Realworld Energy Management Systems
Beichter, S.; Beichter, M.; Nöthel, A.; Pinter, J.; Galenzowski, J.; Förderer, K.; Wiegel, F.; Moser, T.; Waczowicz, S.; Mikut, R.; Hagenmeyer, V.
2025. 2025 IEEE 16th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), 799–804, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/PEDG62294.2025.11060365

Probabilistic Day-Ahead Battery Scheduling based on Mixed Random Variables
Pinter, J.; Zahn, F.; Beichter, M.; Mikut, R.; Hagenmeyer, V.
2025, May 19. 8th DTU-PES Summer School (2025), Copenhagen, Denmark, May 18–23, 2025

Decision-Focused Fine-Tuning of Time Series Foundation Models for Dispatchable Feeder Optimization
Beichter, M.; Friederich, N.; Pinter, J.; Werling, D.; Phipps, K.; Beichter, S.; Neumann, O.; Mikut, R.; Hagenmeyer, V.; Heidrich, B.
2025. arxiv. doi:10.48550/arXiv.2503.01936

Probabilistic Day-Ahead Battery Scheduling based on Mixed Random Variables for Enhanced Grid Operation
Pinter, J.; Zahn, F.; Beichter, M.; Mikut, R.; Hagenmeyer, V.
2024. arxiv. doi:10.48550/arXiv.2411.12480

Concepts for Automated Machine Learning in Smart Grid Applications
Meisenbacher, S.; Pinter, J.; Martin, T.; Hagenmeyer, V.; Mikut, R.
2021. Proceedings - 31. Workshop Computational Intelligence : Berlin, 25. - 26. November 2021. Hrsg.: H. Schulte; F. Hoffmann; R. Mikut, 11–35, KIT Scientific Publishing