Predictive Information in Reported Forced Outages for Short-Term Power Generation Forecasting: A Surrogate-Controlled Spatio-Temporal Forecasting Study

Reported forced outages may provide forecast-relevant information for short-term power generation forecasting. This study examines whether their predictive contribution in spatio-temporal Graph Neural Networks (GNNs) can be distinguished from the generic effect of adding an input channel. Hourly ENTSO-E data for Poland were evaluated over 12 quarters of 2022–2024 using a rolling-origin design, eight random seeds per quarter, and four architectures. A surrogate-controlled design decomposed the conventional ablation contrast into an information component and a channel component. The pooled information component was 0.77 MW (95% confidence interval [−0.60, 2.15]), while the channel component was −1.52 MW (95% confidence interval [−3.16, 0.11]); both varied substantially across quarters. Under matched information and equal tuning, no graph architecture differed significantly from the non-graph Temporal Fusion Transformer. However, because the original publication times of outage messages could not be reconstructed, the results do not establish that outage information was available at the corresponding forecast origins or that it provides predictive value in real time. The study evaluates forecast accuracy only; operational effects are not assessed.