Machine Learning for German Redispatch Forecasting under Data Delays and Temporal Distribution Shift
Public redispatch records provide empirical data for grid congestion forecasting, but delayed reporting, zero-inflated distributions, and temporal shift present major modeling challenges. We assess the accuracy and reliability of probabilistic machine-learning forecasts using published German transmission records under experimentally imposed information-age constraints. The benchmark evaluates eight daily series of upward and downward intervention energy across four German transmission system operators from 2021 to 2024 (48,242 eligible records; 354 evaluation dates in 2024). We compare seasonal empirical, regularized autoregressive (ARX), quantile LightGBM, GRU, and Transformer models un...
| Authors | Faraz Shamim, Faris Shamim |
|---|---|
| Primary category | eess.SY |
| Published | 6 October 2026 |
| Last updated at source | 6 October 2026 |
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