A short-term wind power forecasting model based on CUR

Abstract Wind power forecasting plays a crucial role in the contemporary renewable energy system. During the process of forecasting wind power, the establishment of LSTM models requires a lot of time and effort, and the interpretability of prediction results is poor, making it difficult to understand and verify the results. To accomplish interpretable and precise wind power predictions, this paper introduces a wind power prediction algorithm model leveraging CUR matrix decomposition. The CUR matrix decomposition method first obtains the original matrix A (wind power data matrix). The statistical influence scores of rows and columns in A are calculated, and several columns and rows with higher scores are selected to form a low dimensional matrix C and R. Matrix C contains the main characteristic factors that affect wind power, while matrix R contains time series features. Then, the matrix U is approximated by A, C, and R to transform the preference feature extraction problem in high-dimensional space into a matrix analysis problem in low-dimensional space, making it more interpretable and accurate. The efficacy of the wind power forecasting approach utilizing CUR matrix decomposition is assessed and confirmed on openly accessible datasets. The results indicate that the CUR matrix decomposition method has good prediction accuracy and interpretability.