Ultra-Short-Term Multi-Horizon Point and Probabilistic Wind Power Forecasting with Power-Increment Enhancement and Signed Adaptive Conformal Inference

Abstract

Local power changes, weak-output states, and changing interval coverage complicate ultra-short-term wind power forecasting. This study develops power-increment enhancement and signed adaptive conformal inference (PIE-SACI) to address these challenges within a joint point and probabilistic forecasting framework. XGBoost predicts power increments relative to the latest observation and is combined with a TimeXer temporal forecast; a Persistence-based low-power fallback (LPF) adjusts forecasts under weak-output conditions. Conditional residual quantiles provide base intervals, which are calibrated using the conventional signed CQR score, horizon-specific miscoverage states, and score histories updated only when target labels become available. Across 15, 30, and 60 min horizons on the primary dataset, the complete model reduces RMSE by 3.35–5.46% relative to TimeXer. Matched controls show that TimeXer adds only a modest incremental benefit when LPF is held fixed. At 90% nominal coverage, Signed-ACI achieves coverage errors of 0.0952–0.3998 percentage points and the lowest mean Winkler score among the compared methods at each horizon. Ablation and coverage-level analyses support the combined use of adaptive feedback and signed corrections. Independently retrained Hackberry experiments at 1, 2, and 4 h horizons show similar performance trends under the stated offline data-repair procedure. The results support the empirical integration of increment prediction, low-power adjustment, and delayed-feedback calibration for multi-horizon wind forecasting.