Wind Power Prediction Based on LSTM and Self-Attention Mechanism

With the intensification of global climate change and energy crises, wind energy, as a clean and renewable energy source, has gradually become a crucial component in the energy sector. However, the intermittent and unstable nature of wind power generation poses significant challenges to accurately predicting the power output of wind turbines. This study proposes a wind power prediction model combining Long Short-Term Memory (LSTM) networks and Self-Attention mechanisms. LSTM net- works effectively capture long-term dependencies in time series through their gat- ing mechanisms, while the Self-Attention mechanism dynamically adjusts attention to critical time steps, further enhancing prediction accuracy. Experimental validation on real-world wind power datasets demonstrates that the LSTM + Attention model outperforms traditional RNN and LSTM models in terms of training loss, validation loss, and prediction accuracy, particularly in reducing prediction errors and improving accuracy. The results indicate that the LSTM model integrated with Self-Attention ef- fectively addresses complex nonlinear features in wind power prediction, enhancing both generalization capability and prediction precision. This model provides an ef- fective solution for wind power prediction and holds significant application value for optimizing grid dispatch and management, as well as improving the competitiveness of wind energy in the energy market.