Wind Speed Prediction Based on CEEMDAN-ESN and Error Correction Strategy

In recent years, wind energy has developed rapidly and has become one of the essential new energy sources. Due to the intermittent and randomness of wind energy, it brings massive challenges to tasks such as wind power integration. Wind speed is one of the direct elements affecting wind energy characteristics, and its future state is crucial for wind energy utilization. Therefore, accurate wind speed prediction is of profound significance for wind energy prediction and grid connection. Therefore, a wind speed prediction method based on the complete adaptive noise integrated empirical mode decomposition recovery state network (CEEMDAN-ESN), and error correcting is proposed. Based on the experiment, the proposed method which is aimed at predicting the wind speed of wind farms in Baicheng brings to an accuracy that the error is less than 7%.