Wind-storage combined system based on just-in-time-learning prediction model with dynamic error compensation

Hybrid energy storage configuration methodology, taking into account the accumulation of wind farm forecast deviations

“…To reduce the impact of prediction bias, Yang et al (2023) proposed a wind storage combined system based on a real-time learning prediction model with dynamic error compensation, which improves the accuracy of wind power prediction and reduces the uncertainty of wind farm output through the wind speed curve scoring (WSCS) model. Yuan et al (2021) proposed a scenariobased prediction method to address the uncertainty of wind farm output; this method involves generating a dataset using the generative adversarial network method and applying a genetic algorithm to predict multi-objective scenarios.…”