A transformer-LSTM network enhanced by EEMD for ultra-short-term wind power forecasting

• The AI-driven model integrates signal decomposition with deep learning techniques. • An attention-based Transformer–LSTM network enhances renewable energy forecasting. • Multi-scale feature extraction significantly improves the accuracy of wind power prediction. • Intelligent optimization strategies ensure model robustness under variable grid conditions. • The proposed framework advances AI applications in smart and sustainable energy systems. This study aims to improve the dispatch safety and economic efficiency of grid-connected wind power systems by addressing the limitations of traditional ultra-short-term forecasting methods, particularly their inadequate extraction of multi-scale features and limited forecasting accuracy. A short-term wind power forecasting model that integrates signal decomposition with deep learning is proposed. The model first applies Ensemble Empirical Mode Decomposition (EEMD) to the raw wind power data to reduce non-stationarity and extract multi-scale features. A lightweight Transformer attention mechanism is then employed to model global dependencies, and Long Short-Term Memory (LSTM) networks are incorporated to capture the temporal dynamics of the sequence. The final power forecasting is generated through a fully connected layer. Finally, the Alpha Evolution (AE) algorithm is employed to optimize the model's hyperparameters. Experiments on multiple datasets show that the proposed model outperforms traditional machine learning and deep learning approaches across various evaluation metrics. It achieves higher fitting accuracy, confirming its effectiveness and robustness in multi-scale feature extraction and wind power forecasting.