Regional Short‐Term Wind Power Prediction Based on CEEMDAN‐FTC Feature Mapping and EC‐TCN‐BiLSTM Deep Learning

Abstract

ABSTRACT Regional‐scale holistic wind power prediction (WPP) is pivotal to securing the safety, stability, and economic efficiency of power systems. To improve the accuracy of regional short‐term WPP, a method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), fine‐to‐coarse (FTC) feature mapping, and error compensation‐temporal convolutional network‐bidirectional Long short‐term memory network (EC‐TCN‐BiLSTM) is proposed in this paper. First, the regional input features, encompassing data from numerous wind farms, are decomposed using the CEEMDAN algorithm to extract intrinsic mode functions (IMFs) and residuals at different time scales. Second, the decomposed IMFs and residuals are reconstructed using the adaptive FTC feature mapping technique, forming a high‐dimensional feature set in the time‐frequency domain, which boasts fewer features than the original set, thus diminishing the computational intricacy of the prediction model. Third, by combining the strengths of TCN and BiLSTM neural networks, the temporal and spatial correlations of input features can be captured effectively. Fourth, the integration of the EC module corrects the systematic errors of the prediction results, thereby further improving the prediction accuracy. Finally, case studies elucidate the efficacy of the proposed WPP method, illustrating a 0.41%–2.4% diminution in 24‐h‐ahead root mean square error (RMSE) and a 0.68%–2.63% reduction in 96‐h‐ahead RMSE relative to conventional methodologies.