Wind Power Forecasting Based on Multi-Graph Neural Networks Considering External Disturbances

Wind power forecasting is challenging because of complex, nonlinear relationships between inherent patterns and external disturbances. Though much progress has been achieved in deep learning approaches, existing methods cannot effectively decompose and model intertwined spatio-temporal dependencies. Current methods typically treat wind power as a unified signal without explicitly separating inherent patterns from external influences, so they have limited prediction accuracy. This paper introduces a novel framework GCN-EIF that decouples external interference factors (EIFs) from inherent wind power patterns to achieve excellent prediction accuracy. Our innovation lies in the physically informed architecture that explicitly models the mathematical relationship: P ( t ) = P inherent ( t ) + EIF ( t ) . The framework adopts a three-component architecture consisting of (1) a multi-graph convolutional network using both geographical proximity and power correlation graphs to capture heterogeneous spatial dependencies between wind farms, (2) an attention-enhanced LSTM network that weights temporal features differentially based on their predictive significance, and (3) a specialized Conv2D mechanism to identify and isolate external disturbance patterns. A key methodological contribution is our signal decomposition strategy during the prediction phase, where an EIF is eliminated from historical data to better learn fundamental patterns, and then a predicted EIF is reintroduced for the target period, significantly reducing error propagation. Extensive experiments across diverse wind farm clusters and different weather conditions indicate that GCN-EIF achieves an 18.99% lower RMSE and 5.08% lower MAE than state-of-the-art methods. Meanwhile, real-time performance analysis confirms the model’s operational viability as it maintains excellent prediction accuracy (RMSE < 15) even at high data arrival rates (100 samples/second) while ensuring processing latency below critical thresholds (10 ms) under typical system loads.