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Karlsruhe Institute of Technology
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TSFMs can achieve competitive forecasting performance in critical infrastructure applications while also providing interpretable explanations that align with established domain knowledge.
Foundation models don't always win: task-specific models can rival or even beat them in electricity price forecasting, especially with clever feature engineering or transfer learning.
LLMs know the lingo of industrial control systems, but can't yet reliably hack them without specialized tools.
A multi-agent LLM system can fuse heterogeneous data sources to accurately classify building ages from satellite imagery, enabling better urban energy planning despite class imbalances in historical building cohorts.
Skip expensive building-level data collection: HeatPrompt uses satellite images and vision-language models to accurately map urban heat demand.
