A Short-term Wind Power Prediction Model Based on Graph Convolutional Neural Network-bidirectional Gated Recurrent Unit and Attention Mechanism

Accurate prediction of wind power is of great significance to the stable operation of power systems. To address the issue that traditional combinatorial models face the challenges in fully exploring the potential dependencies among variables and exhibit low accuracy in wind power prediction when dealing with high-dimension and largescale data, a model, incorporating a graph convolutional neural network-bidirectional gated cyclic unit and an attention mechanism, is proposed for short-term wind power prediction. The model takes numerical weather prediction (NWP) data and wind power historical data as input. It initially employs Pearson correlation analysis to filter features, followed by the utilization of graph convolutional neural network (GCN) with the help of residual connection to mine spatial feature relationships via network and graph. Subsequently, a bidirectional gated recurrent unit (BiGRU) is employed to mine the time-series features of historical data. Finally, an attention mechanism (AM) is introduced to assign weights to achieve short-term wind power prediction. The experimental results demonstrate that this method exhibits superior prediction accuracy in both single-step and multi-step prediction compared to other methods.