Short-Term Wind Power Forecasting under Extreme Weather Based on Improved TimeGAN and GAT-Crossformer
To overcome the combined issues of intricate weather feature dynamics and insufficient data under extreme conditions, a short-term wind power prediction framework is presented, leveraging a modified time-series generative adversarial network (TimeGAN) alongside a GAT-Crossformer network. First, raw data corresponding t...