Abstract:

Improving the accuracy of wind power prediction is crucial for ensuring safe and stable operation of the power grid. However， wind power exhibits high randomness and volatility， and traditional prediction methods have limitations in feature extraction and modeling capabilities. Therefore，CEEMDAN-DBO-VMD-TCN-BiGRU， a short-term wind power prediction model integrating complete ensemble empirical mode decomposition with adaptive noise（CEEMDAN）， dung beetle optimizer（DBO）algorithm， variational mode decomposition（VMD）， temporal convolutional network（TCN）， and bidirectional gated recurrent unit（BiGRU） was proposed. CEEMDAN was used to decompose the original wind power data， extracting intrinsic mode functions（IMFs） to capture key features of the time series. The IMFs were divided into high-frequency， medium-frequency， and low-frequency components using sample entropy and K-means clustering. The high-frequency components were selected for secondary decomposition using DBO-optimized VMD to improve feature extraction effectiveness and reduce computational complexity. All components were normalized and then input into the TCN-BiGRU combined model for prediction. The prediction results of each component were superimposed and denormalized to obtain the final prediction value. Experimental results showed that compared with benchmark models， the proposed model had the best prediction accuracy， verifying its effectiveness， stability， and application potential.

Key words: wind power prediction, complete ensemble empirical mode decomposition with adaptive noise, dung beetle optimizer algorithm, variational mode decomposition, sample entropy, K-means clustering, temporal convolutional network, bidirectional gated recurrent unit