11 citations · 32 across the 9 of their papers we have counts for
9 papers
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…
Optimizing Small Language Models for In-Vehicle Function-Calling
Yahya Sowti Khiabani, Farris Atif, Chieh Hsu +7
We propose a holistic approach for deploying Small Language Models (SLMs) as function-calling agents within vehicles as edge devices, offering a more flexible and robust alternativ…
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…
tsbootstrap: Enhancing Time Series Analysis with Advanced Bootstrapping Techniques
Sankalp Gilda, Benedikt Heidrich, Franz Kiraly
In time series analysis, traditional bootstrapping methods often fall short due to their assumption of data independence, a condition rarely met in time-dependent data. This paper…
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…
Creating Probabilistic Forecasts from Arbitrary Deterministic Forecasts using Conditional Invertible Neural Networks
Kaleb Phipps, Benedikt Heidrich, Marian Turowski +3
In various applications, probabilistic forecasts are required to quantify the inherent uncertainty associated with the forecast. However, numerous modern forecasting methods are st…
AutoPV: Automated photovoltaic forecasts with limited information using an ensemble of pre-trained models
Stefan Meisenbacher, Benedikt Heidrich, Tim Martin +2
Accurate PhotoVoltaic (PV) power generation forecasting is vital for the efficient operation of Smart Grids. The automated design of such accurate forecasting models for individual…
pyWATTS: Python Workflow Automation Tool for Time Series
Benedikt Heidrich, Andreas Bartschat, Marian Turowski +7
Time series data are fundamental for a variety of applications, ranging from financial markets to energy systems. Due to their importance, the number and complexity of tools and me…