Short-term wind power prediction based on preprocessing and improved secondary decomposition

Abstract

With more locations for wind generation, the grid's dependability is degraded. This paper presents a state-of-art combined wind power prediction model, including data preprocessing, improved secondary decomposition, and deep learning. A density-based spatial clustering of applications with noise was used primarily to identify and address irrational data and then correct them using k-nearest neighbor. Later, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was used to decompose the original wind power time series into several intrinsic mode functions (IMFs), and the variational mode decomposition (VMD) was adopted for further decomposition, due to its high irregularity and instability, of the first two components. Finally, a long short-term memory (LSTM) was employed to predict each component. The proposed model was then applied to two wind farms in Turkey and France. The experimental findings are as follows: (1) The data preprocessing scheme proposed in this paper can improve the predicted results. After data preprocessing, mean absolute error (MAE) and root mean squared error (RMSE) have declined by 10.73% and 10.20% on average, respectively. (2) The improved predictions were greater than the common secondary decomposition. The MAE and RMSE of improved CEEMDAN-VMD-LSTM were down by 14.77% and 15.12% on average, compared with CEEMDAN-VMD-LSTM, respectively.