DHGAR: Multi-Variable-Driven Wind Power Prediction Model Based on Dynamic Heterogeneous Graph Attention Recurrent Network

Accurate and stable wind power prediction is essential for effective wind farm capacity management and grid dispatching. Wind power generation is influenced not only by historical data, but also by turbine conditions and external environmental factors, such as weather. Although deep learning has made significant progress in the field of wind power forecasting, it often fails to account for two key characteristics of the data: dynamic variability and heterogeneity. Specifically, the influence of external variables on wind power changes over time, and due to the diverse nature of the information carried by different variables, simple weighted fusion approaches are insufficient to fully integrate heterogeneous data. To address these challenges, this paper introduces a dynamic heterogeneous graph attention recurrent network (DHGAR), which incorporates dynamic graphs, heterogeneous graph attention mechanisms, and gated recurrent units. Dynamic graphs capture real-time associations between wind power and external variables, while heterogeneous graph attention allows for more effective aggregation of diverse information. These two components are integrated into the gated recurrent units, replacing traditional fully connected layers to better capture temporal dependencies in the wind power time series. Experimental results on three real-world datasets demonstrate the superior performance and practical applicability of the proposed model.