A Hybrid Informer–TCN-Quantile Framework with IOOA-Based Hyperparameter Optimization for Wind Power Interval Forecasting

Wind power interval forecasting remains challenging due to the uncertainty and strong variability of wind generation. To capture temporal dependency and predictive uncertainty, this paper proposes a hybrid interval forecasting framework that integrates an Informer-based point prediction model with a temporal convolutional network (TCN) conditional quantile model. The Informer is used to generate deterministic forecasts, while the TCN models the temporal dependency of prediction residuals and estimates conditional quantiles for interval construction. To further improve interval quality, an improved osprey optimization algorithm (IOOA) is introduced to optimize key TCN hyperparameters. The Coverage–Width Criterion (CWC) on the validation set is adopted as the optimization objective for hyperparameter tuning and adaptive quantile-pair selection. To maintain the nominal 90% confidence level, candidate quantile pairs are constrained to have a fixed quantile span of 0.90. Experiments on real-world wind power datasets demonstrate that, when averaged across the two wind farms, the proposed framework achieves a prediction interval coverage probability (PICP) of 0.910, satisfying the nominal coverage level of 90%, and a mean prediction interval width (MPIW) of 7.48, the lowest among all compared methods. Specifically, it reduces the mean interval width by 8.89–28.35% relative to the benchmark models, indicating that the proposed framework generates sharper prediction intervals without compromising coverage reliability and achieves a better trade-off between reliability and sharpness.