Short-term regional wind power forecasting based on spatial–temporal correlation and dynamic clustering model

Abstract

Short-term regional wind power forecasting (WPF) is essential for enhancing the power grid’s robustness. In the field of regional short-term power forecasting, numerical weather forecasts (NWP) and regional wind-power historical data have been commonly used in power forecasting. In this paper, a regional wind power prediction model based on the spatial and temporal correlation of meteorological resources is proposed to predict the wind power in the next seven days. First, combining variational mode decomposition and Granger causality test (VMD-GCT) to analyze the NWP-related meteorological factors to the wind power components in different bands to obtain the screening of the NWP-related factors. Then, the correlation analysis technique, which uses the physical characteristics of the wind speed time lag in a region, dynamically analyzes the spatial correlation time lag between a wind farm in the region and other wind farms, and uses the DBSCAN (density-based spatial clustering of applications with noise) algorithm to dynamically divide wind power clusters in the wind power forecast cycle, thus laying the foundation for the input data of the prediction model. Finally, the short-term power forecasting in the region is performed by a combined deep learning model. The results show that the proposed method can significantly improve the accuracy and efficiency of the wind power prediction for clusters at different wind speeds. The proposed method is of great significance for the short-term prediction of wind power in a region.