Medium and Long Term Wind Power Prediction Based on Graph Convolutional Network and Wind Velocity Differential Fitting - Zihan CHEN; Wei TENG; Xuefeng XU; Xian DING; Yibing LIU (Zhongguo dianli)
In order to make full use of the prior relationships among data features and improve the prediction accuracy of medium and long term wind power at wind farms, a medium and long term wind power prediction model based on graph convolution neural network (GCN), wind velocity differential fitting (DF), and particle swarm optimization (PSO) is proposed. By analyzing the whole process of wind power generation, the influencing factors of wind power and the interrelation among them are explored, and the GCN model is built. The wind velocity and power utilization efficiency are fitted respectively. The wind power is calculated by combining with the wind velocity–power calculation model based on DF. The loss of the model includes three parts: power loss, wind velocity loss and power utilization efficiency loss. PSO algorithm is used to determine the appropriate weight for the three losses. The on-site examples of two wind farms show that the relative root mean square error of the wind power prediction model in the next 10 days is 11.44% and 13.09%, respectively, which has a high prediction accuracy.