Wind Farm Combination Forecasting Model Based on Dynamic Graph Attention
In order to realize efficient scheduling of wind farm energy use management and fully extract the potential relationship between spa-tial and temporal characteristics of multiple sites,a multi-site short-term wind power spatio-temporal combination prediction model based on dynamic graph convolution and graph attention is proposed.Firstly,graph convolution is used to realize neighbor aggregation of temporal fea-tures among multiple sites,and graph attention mechanism is used to enhance its ability to extract spatial features.At the same time,in view of the problem that the traditional model cannot handle the real-time changes of the graph node correlation,firstly,the adjacency matrix is dy-namically constructed according to the correlation coefficient and distance between the sites during the graph convolution process;secondly,the gated cycle unit is used to process the context information of the output of the dynamic graph convolution;finally,the wind power predic-tion is completed.The experimental results show that the proposed combined model is optimal in terms of prediction accuracy,stability and multi-step prediction performance.
short-term wind power forecastdynamic correlationgraph convolution neural networkattentional mechanismgated recur-rent unit
万方数据
维普
