3 papers
cs.LG2025
Load Forecasting for Households and Energy Communities: Are Deep Learning Models Worth the Effort?
Lukas Moosbrugger, Valentin Seiler, Philipp Wohlgenannt +4
Energy communities (ECs) play a key role in enabling local demand shifting and enhancing self-sufficiency, as energy systems transition toward decentralized structures with high sh…
cs.LG2024
Improve Load Forecasting in Energy Communities through Transfer Learning using Open-Access Synthetic Profiles
Lukas Moosbrugger, Valentin Seiler, Gerhard Huber +1
According to a conservative estimate, a 1% reduction in forecast error for a 10 GW energy utility can save up to $ 1.6 million annually. In our context, achieving precise forecasts…
math.OC2024
Towards Efficient Aggregation of Storage Flexibilities in Power Grids
Emrah Öztürk, Kevin Kaspar, Timm Faulwasser +3
The increasing penetration of volatile renewables combined with increasing demands poses a challenge to modern power grids. Furthermore, distributed energy resources and flexible d…