Hierarchical Dynamic Hypergraph Learning for Ultrashort-Term Wind Farm Cluster Power Forecasting With Input-Aware Meteorological Integration Method

Accurate multistep ultrashort-term wind power forecasting for wind farm clusters is essential for secure and flexible power system operation. However, existing forecasting methods often rely on pairwise spatial correlations or static graph structures, which limits their ability to represent multiscale spatial dependencies and transient fluctuation patterns among wind farms. In addition, the temporal mismatch between future numerical weather prediction (NWP) information and different forecasting horizons may further degrade multistep forecasting accuracy. To address these challenges, this study proposes a hierarchical dynamic hypergraph forecasting framework with input-aware meteorological modeling for wind farm cluster power forecasting. Specifically, four types of hyperedges are constructed to describe local correlations, regional coordinated structures, global similarity patterns, and transient response relationships among wind farms. A multichannel architecture is then designed to jointly extract historical power information, future NWP features, graph-based pairwise spatial representations, and hypergraph-based high-order spatial representations. Furthermore, an input-aware modeling strategy is developed to adaptively incorporate horizon-related meteorological information, thereby reducing temporal mismatch in multistep forecasting. The proposed method is validated using real-world data from large-scale wind farm clusters in China. Experimental results averaged over multiple independent trials show that the proposed method achieves lower NRMSE and NMAE than representative transformer-based and graph-based benchmark models, with an average absolute NRMSE reduction of 1.5% over the benchmark models and a 13.1% relative NRMSE reduction compared with the strongest baseline under the tested setting. The results indicate that the proposed framework can improve multistep forecasting accuracy and curve-tracking consistency.