Machine Learning for German Redispatch Forecasting under Data Delays and Temporal Distribution Shift

The study evaluates probabilistic machine‑learning models for forecasting German grid redispatch volumes under data‑delay constraints. Using 48,242 records from 2021‑2024, boosted‑tree LightGBM with rolling calibration achieved the best performance (nWIS 0.7767), outperforming autoregressive and seasonal baselines. Neural models with zero‑censored outputs performed similarly but revealed undercoverage during high‑volume events.

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• llms
• benchmarks