Short-Term Wind Power Forecasting Based on CNN–LSTM Model With CEEMDAN–VMD Decomposition

Accurate wind power forecasting is essential for the reliable and efficient operation of renewable energy–dominated power systems, as it directly affects power system scheduling and grid stability. Nevertheless, the inherent intermittency, strong nonlinearity, and stochastic characteristics of wind power generation pose significant challenges to short-term forecasting accuracy. To tackle these issues, this paper develops a hybrid forecasting framework that integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Variational Mode Decomposition (VMD), and a deep learning predictor based on Convolutional Neural Networks and Long Short-Term Memory networks (CNN–LSTM). Specifically, CEEMDAN is first employed to decompose the original wind power time series into multiple intrinsic mode functions at different temporal scales. Subsequently, VMD is applied to the high-frequency components to further suppress noise and enhance signal stationarity. Each decomposed subsequence is then modeled and predicted using a CNN–LSTM architecture, and the final wind power forecast is obtained through reconstruction of all predicted components. Extensive experiments conducted on real-world wind farm data that the proposed hybrid model consistently outperforms several benchmark methods in terms of forecasting accuracy, thereby verifying its effectiveness and practical applicability for short-term wind power forecasting.