Short-term Power Prediction Method of Wind Power Cluster Based on CBAM-LSTM
The accurate prediction of wind power is of great significance for China to achieve the goal of'carbon peak'and 'carbon neutrality'.Traditional wind power prediction methods often ignore the long-term dependence and spatial correlation in time series data,resulting in inaccurate prediction results.In order to solve this problem,this paper proposes a model combining Convolutional Block Attention Module(CBAM)and Long Short-Term Memory(LSTM).Firstly,CBAM is used to extract the characteristics of wind power time series data and the spatial characteristics contained in numerical weather prediction.This module can adaptively learn important features in time and space.Then,the extracted features are input into the LSTM layer structure for power prediction.In order to verify the effectiveness of the proposed method,a data set of a wind farm in Jilin Province,China is used for verification.The experimental results show that compared with other power prediction methods used in this paper,the mean absolute error(MAE)of the proposed method is reduced by an average of 2.67%.The coefficient of determination(R-Square,R2)increased by an average of 23%.The root mean square error(RMSE)decreased by 2.69%on average.
NETL
NSTL
万方数据
