Reliable ultra-short-term wind power forecasting via robust preprocessing, optimized deep learning, and bootstrap interval prediction

This paper presents an integrated framework for reliable ultra-short-term wind power forecasting and uncertainty quantification. The framework integrates robust data preprocessing, two-stage feature selection, and lag selection to construct quality inputs. At its core, it combines a bidirectional long short-term memory (BiLSTM) model with whale optimization algorithm (WOA)-based hyperparameter optimization to identify an optimized architecture and training configuration. To assess generalizability, the forecasting core is validated across two scales: a multivariate wind turbine dataset from Tenerife, Spain, and a univariate regional wind power dataset from Belgium. Results show an 80.02% reduction in normalized root mean square error (nRMSE) relative to the persistence model, while maintaining competitive accuracy against state-of-the-art deep learning models. Robustness is assessed through multi-step forecasts up to 60 min and statistical significance tests. Distribution-free prediction intervals generated by bootstrap resampling provide well-calibrated uncertainty information across both sites, supporting operational decision-making in wind-integrated power grids.