Power forecasting method of ultra-short-term wind power cluster based on the convergence cross mapping algorithm

Abstract

The development of wind power clusters has scaled in terms of both scale and coverage, and the impact of weather fluctuations on cluster output changes has become increasingly complex. Accurately identifying the forward-looking information of key wind farms in a cluster under different weather conditions is an effective method to improve the accuracy of ultrashort-term cluster power forecasting. To this end, this paper proposes a refined modeling method for ultrashort-term wind power cluster forecasting based on a convergent cross-mapping algorithm. From the perspective of causality, key meteorological forecasting factors under different cluster power fluctuation processes were screened, and refined training modeling was performed for different fluctuation processes. First, a wind process description index system and classification model at the wind power cluster level are established to realize the classification of typical fluctuation processes. A meteorological-cluster power causal relationship evaluation model based on the convergent cross-mapping algorithm is proposed to screen meteorological forecasting factors under multiple types of typical fluctuation processes. Finally, a refined modeling method for a variety of different typical fluctuation processes is proposed, and the strong causal meteorological forecasting factors of each scenario are used as inputs to realize high-precision modeling and forecasting of ultra-short-term wind cluster power. An example analysis shows that the short-term wind power cluster power forecasting accuracy of the proposed method can reach 88.55 %, which is 1.57–7.32 % higher than that of traditional methods.