9 citations · 19 across the 11 of their papers we have counts for
7 papers · 1 filter
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…
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…
MLOps for Scarce Image Data: A Use Case in Microscopic Image Analysis
Angelo Yamachui Sitcheu, Nils Friederich, Simon Baeuerle +3
Nowadays, Machine Learning (ML) is experiencing tremendous popularity that has never been seen before. The operationalization of ML models is governed by a set of concepts and meth…
Transformer Training Strategies for Forecasting Multiple Load Time Series
Matthias Hertel, Maximilian Beichter, Benedikt Heidrich +4
In the smart grid of the future, accurate load forecasts on the level of individual clients can help to balance supply and demand locally and to prevent grid outages. While the num…
ProbPNN: Enhancing Deep Probabilistic Forecasting with Statistical Information
Benedikt Heidrich, Kaleb Phipps, Oliver Neumann +3
Probabilistic forecasts are essential for various downstream applications such as business development, traffic planning, and electrical grid balancing. Many of these probabilistic…
EasyMLServe: Easy Deployment of REST Machine Learning Services
Oliver Neumann, Marcel Schilling, Markus Reischl +1
Various research domains use machine learning approaches because they can solve complex tasks by learning from data. Deploying machine learning models, however, is not trivial and…