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

Large-scale renewable energy deployment introduces pronounced volatility into the electricity system, turning grid operation into a complex stochastic optimization problem. Accurate electricity price forecasting (EPF) is essential not only to support operational decisions, such as optimal bidding strategies and balancing power preparation, but also to reduce economic risk and improve market efficiency. Probabilistic forecasts are particularly valuable because they quantify uncertainty stemming from renewable intermittency, market coupling, and regulatory changes, enabling market participants to make informed decisions that minimize losses and optimize expected revenues. However, it remains an open question which models to employ to produce accurate forecasts. Should these be task-specific machine learning (ML) models or Time Series Foundation Models (TSFMs)? In this work, we compare four models for day-ahead probabilistic EPF (PEPF) in European bidding zones: a deterministic NHITS backbone with Quantile-Regression Averaging (NHITS+QRA) and a conditional Normalizing-Flow forecaster (NF) are compared with two TSFMs, namely Moirai and ChronosX. On the one hand, we find that TSFMs outperform task-specific deep learning models trained from scratch in terms of CRPS, Energy Score, and predictive interval calibration across market conditions. On the other hand, we find that well-configured task-specific models, particularly NHITS combined with QRA, achieve performance very close to TSFMs, and in some scenarios, such as when supplied with additional informative feature groups or adapted via few-shot learning from other European markets, they can even surpass TSFMs. Overall, our findings show that while TSFMs offer expressive modeling capabilities, conventional models remain highly competitive, emphasizing the need to weigh computational expense against marginal performance improvements in PEPF.

Utilities publish Estimated Times of Restoration (ETRs) for customer-facing storm outages, and their accuracy governs whether customers can make sound decisions about food, medical equipment, and relocation. Prior work treats ETR as static tabular regression in which each outage contributes one record, discarding the fact that every development of an outage, from crew assignment through dispatch, suspension, damage assessment and partial restoration, is recorded as a revision. We reformulate ETR prediction as longitudinal tabular regression and introduce a Longitudinal Tabular Transformer (LTT), an axial-attention model that consumes the revisions preceding a prediction and issues a refined estimate at every one. On 242{,}928 storm-attributed outages from a cohort of 526{,}468 filtered events and 10.0 million revisions at six operating companies, LTT reduces customer-weighted asymmetric error at all six, by a median of 36.9,% against the estimates the utilities published during the same storms and 11.3,% against the strongest learned baseline at each. It is the only method improving on the incumbent's satisfaction impact at all six companies while also reducing root mean squared error at all six. Stratification by revision index shows that LTT error is largest at the first revision, where no history is available, and falls monotonically as revisions accumulate.

Time-series foundation models (TSFMs) provide forecasts for operational decisions, but accuracy alone does not determine their value. Evaluating agents that use these models requires measuring decision quality and forecast cost. FWBench evaluates this capability on 1,251 electricity and cycle-hire cases using fixed forecast tools and simulated capacity contracts. Agents select models, histories and horizons, then submit capacities to minimize a stated loss-cost objective. We evaluated two hosted and eight local configurations, including small language models, and tested local models with and without TSFMs. GPT-6 Astra bought inexpensive short-horizon forecasts selectively, using 2.5% of the budget, and outperformed fixed policies when the saved decisions were scored with three loss-cost weightings. FWBench enables reproducible evaluation of how language models select and use time-series forecasts to make decisions under cost constraints.