A Spatiotemporal Coupling Calculation-Based Short-Term Wind Farm Cluster Power Prediction Method

Accurate short-term wind power prediction is of great significance to the real-time dispatching of power systems and the development of wind power generation plans. However, existing methods for wind power prediction have the following problems: 1) some studies aim at predicting wind power for a single wind farm, ignoring the correlations of the adjacent wind farms; 2) many studies tend to convert wind speed forecast results to wind power, increasing the conversion error; 3) almost all studies place emphasis on the capture of spatiotemporal features, neglecting the influence of spatiotemporal coupling. Therefore, to solve the above questions, this work proposes an adaptive graph neural network based on spatiotemporal attention calculation for short-term wind farm cluster power prediction, using only wind power data. Firstly, a dynamic undirected graph is established to sufficiently learn prior knowledge of spatial relationships. Next, the spatiotemporal coupling relationship and global temporal correlation between data can be computed by performing spatiotemporal cross-attention and temporal self-attention, respectively. Finally, a novel hybrid loss function is proposed to optimize the prediction model accurately. In a case study, compared with other benchmark methods, the proposed method shows excellent overall performance in predicting wind power.