A double-stage method for improving long-term numerical wind speed prediction via chaos-noise decoupling

Abstract

ABSTRACT Reliable 24–48 hr long-term wind-speed forecasts are critical for the safe operation of energy systems. Wind variability is shaped by chaotic dynamics and predictability limits, motivating chaos-aware modeling of Weather Research and Forecasting (WRF) outputs. This paper proposes a double-stage modeling method based on the chaos-noise decoupling, which effectively improves the chaotic modeling performance and prediction accuracy of the WRF for long-term wind speed prediction. In the first step, a dynamic reinforcement learning model guided by the Gramian angular field is used for the WRF ensemble by tracking the time-varying chaotic process of wind speed. In the second step, a closed-loop correction algorithm guided by predictability iteratively extracts the remaining predictable components until the residuals become approximately decorrelated. Experimental results show that (1) Compared to the WRF model, the proposed method yields Lyapunov and Hurst exponents closer to those of the observed data and produces residuals with a median Hurst exponent approaching 0.5, indicating that the residuals are approximately decorrelated. (2) Through comparisons with ablation and existing models, the proposed framework shows improved forecasting accuracy and robustness with over 95% confidence, with long-term prediction horizons of 24 to 48 hours. HIGHLIGHTS A chaos-noise decoupling framework based on predictability is designed to improve the chaotic modeling and prediction performance of WRF. Lyapunov and Hurst exponents are employed to validate dynamic fidelity performance by effective decoupling of chaotic and noisy components. The method is validated using wind-speed data from 10 German stations and an additional public GEFS–ISD-Lite dataset for 24–48 h forecasting.