Probabilistic Wind Energy Forecasting: A GRU-Based Framework with Uncertainty Quantification and Confidence Interval Estimation

Wind energy forecasting is critical for grid stability, yet traditional models inadequately capture the nonlinear, variable nature of wind dynamics. This study presents a probabilistic forecasting framework combining Gated Recurrent Unit (GRU) deep learning with Generalized Integrated Uncertainty (GIU) estimation to provide both accurate predictions and confidence intervals for wind power generation. The GRU architecture processes 24-hour sequences of regional meteorological data (wind speed, direction, temperature, humidity) to capture temporal dependencies. Unlike conventional point-estimate approaches, our model generates quantile predictions (P10, P50, P90) enhanced by sliding-window uncertainty analysis and conformal calibration, yielding operational 95% confidence intervals. Evaluation on real operational data demonstrates strong performance: MAE of 0.126 MW, RMSE of 0.159 MW, and R 2 of 0.7163. The framework provides grid operators with quantified prediction confidence for improved risk management and operational planning, contributing to sustainable renewable energy integration.