Enhanced Wind Power Forecasting Using Graph Convolutional Networks with Ramp Characterization and Error Correction

The strong volatility of wind power presents persistent challenges to the stable operation of power systems, highlighting the critical need for accurate wind power forecasting to ensure system reliability. This study proposes a wind power prediction approach based on graph convolutional networks, incorporating ramp feature recognition and error correction mechanisms. First, an improved ramp event definition is applied to detect and classify wind power ramp events more accurately, thereby reducing misjudgments caused by short-term fluctuations. Then, a GCN-based model is developed to construct graph representations of various ramp scenarios, allowing for the effective modeling of their coupling relationships. This is integrated with a bidirectional long short-term memory network to enhance prediction performance during power fluctuation periods. Finally, a dynamic error feedback correction mechanism is introduced to iteratively refine the prediction results in real time. Experiments conducted on wind power data from a Belgian wind farm show that the proposed method significantly improves prediction stability and accuracy during ramp events, achieving an approximate 28% improvement compared to conventional models, and demonstrates strong multi-step forecasting capability.