6 citations · 9 across the 18 of their papers we have counts for
19 papers
A Benchmark for Electrical Load Forecasting Across Grid Levels: Time-Series Transformers Outperform Established Methods
Matthias Hertel, Sebastian Pütz, Jonathan Kolar +3
Accurate load forecasting at multiple grid levels is essential for future smart grids, ranging from aggregated control area forecasts for balancing supply and demand to forecasts o…
Probabilistic Low-Voltage Peak Load Forecasting with Time Series Foundation Models Evaluated on Application-Oriented Metrics
Benedikt Kaas, Manuel Treutlein, Hannes Benedikt Gerber +5
Low-voltage load forecasting is an important component in current and future energy systems with a high degree of electrification and decentralized generation. However, current for…
Explainable Load Forecasting with Covariate-Informed Time Series Foundation Models
Matthias Hertel, Alexandra Nikoltchovska, Sebastian Pütz +3
Time Series Foundation Models (TSFMs) have recently emerged as general-purpose forecasting models and show considerable potential for applications in energy systems. However, appli…
Energy-Arena: A Dynamic Benchmark for Operational Energy Forecasting
Max Kleinebrahm, Jonathan Berrisch, Philipp Eiser +11
Energy forecasting research faces a persistent comparability gap that makes it difficult to measure consistent progress over time. Reported accuracy gains are often not directly co…
Stochastic Model Predictive Control based on Mixed Random Variables for Economic Energy Management
Janik Pinter, Maximilian Beichter, Ralf Mikut +2
Optimal scheduling of batteries has significant potential to reduce electricity costs and to enhance grid resilience. However, effective battery scheduling must account for both ph…
Knowledge Distillation for Efficient Transformer-Based Reinforcement Learning in Hardware-Constrained Energy Management Systems
Pascal Henrich, Jonas Sievers, Maximilian Beichter +3
Transformer-based reinforcement learning has emerged as a strong candidate for sequential control in residential energy management. In particular, the Decision Transformer can lear…
Real-world energy data of 200 feeders from low-voltage grids with metadata in Germany over two years
Manuel Treutlein, Pascal Bothe, Marc Schmidt +4
The last mile of the distribution grid is crucial for a successful energy transition, as more low-carbon technology like photovoltaic systems, heat pumps, and electric vehicle char…
Explainable time-series forecasting with sampling-free SHAP for Transformers
Matthias Hertel, Sebastian Pütz, Ralf Mikut +2
Time-series forecasts are essential for planning and decision-making in many domains. Explainability is key to building user trust and meeting transparency requirements. Shapley Ad…
ComEMS4Build: Comfort-Oriented Energy Management System for Residential Buildings using Hydrogen for Seasonal Storage
Jovana Kovačević, Felix Langner, Erfan Tajalli-Ardekani +6
Integrating flexible loads and storage systems into the residential sector contributes to the alignment of volatile renewable generation with demand. Besides batteries serving as a…
Averaging favors MPC: How typical evaluation setups overstate MPC performance for residential battery scheduling
Janik Pinter, Maximilian Beichter, Ralf Mikut +2
Residential prosumers with PV-battery systems increasingly manage their electricity exchange with the power grid to minimize costs. This study investigates the performance of Model…
Decision-Focused Fine-Tuning of Time Series Foundation Models for Dispatchable Feeder Optimization
Maximilian Beichter, Nils Friederich, Janik Pinter +7
Time series foundation models provide a universal solution for generating forecasts to support optimization problems in energy systems. Those foundation models are typically traine…
On autoregressive deep learning models for day-ahead wind power forecasting with irregular shutdowns due to redispatching
Stefan Meisenbacher, Silas Aaron Selzer, Mehdi Dado +6
Renewable energies and their operation are becoming increasingly vital for the stability of electrical power grids since conventional power plants are progressively being displaced…
AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability
Stefan Meisenbacher, Kaleb Phipps, Oskar Taubert +4
Optimizing smart grid operations relies on critical decision-making informed by uncertainty quantification, making probabilistic forecasting a vital tool. Designing such forecastin…
Probabilistic Day-Ahead Battery Scheduling based on Mixed Random Variables for Enhanced Grid Operation
Janik Pinter, Frederik Zahn, Maximilian Beichter +2
The increasing penetration of renewable energy sources introduces significant challenges to power grid stability, primarily due to their inherent variability. A new opportunity for…
EAP4EMSIG -- Experiment Automation Pipeline for Event-Driven Microscopy to Smart Microfluidic Single-Cells Analysis
Nils Friederich, Angelo Jovin Yamachui Sitcheu, Annika Nassal +12
Microfluidic Live-Cell Imaging (MLCI) generates high-quality data that allows biotechnologists to study cellular growth dynamics in detail. However, obtaining these continuous data…
Tracking one-in-a-million: Large-scale benchmark for microbial single-cell tracking with experiment-aware robustness metrics
J. Seiffarth, L. Blöbaum, R. D. Paul +6
Tracking the development of living cells in live-cell time-lapses reveals crucial insights into single-cell behavior and presents tremendous potential for biomedical and biotechnol…
Generating peak-aware pseudo-measurements for low-voltage feeders using metadata of distribution system operators
Manuel Treutlein, Marc Schmidt, Roman Hahn +4
Distribution system operators (DSOs) must cope with new challenges such as the reconstruction of distribution grids along climate neutrality pathways or the ability to manage and c…
Open Energy Services -- Forecasting and Optimization as a Service for Energy Management Applications at Scale
David Wölfle, Kevin Förderer, Tobias Riedel +4
This article aims at facilitating the widespread application of Energy Management Systems (EMSs), especially on buildings and cities, in order to support the realization of future…
AI-based automated active learning for discovery of hidden dynamic processes: A use case in light microscopy
Nils Friederich, Angelo Yamachui Sitcheu, Oliver Neumann +4
In the biomedical environment, experiments assessing dynamic processes are primarily performed by a human acquisition supervisor. Contemporary implementations of such experiments f…