风力发电受环境影响显著，具有高度随机性和波动性使得准确预测发电量变得困难。为此，提出了结合多头自注意力机制、卷积神经网络和长短时记忆网络的超短期风电功率预测模型（attention mechanism-convolutional neural network-long short-term memory，AM-CNN-LSTM）。其中，多头自注意力机制根据历史风电功率和气象数据的不同重要性分配权重，生成关键特征的表达，削弱干扰因素。卷积神经网络提取风电功率与气象因素的复杂空间模式和局部依赖关系。长短时记忆网络捕捉时间序列的长期依赖性和动态变化提升预测精度。结果表明，最优时间窗口T=4 h下，模型的平均绝对误差、均方根误差和平均绝对百分比误差分别为13.45 %、19.48 %和3.78 %，优于对比方法LSTM、CNN和CNN-LSTM；比较不同模型最优时间窗口的未来8个样本点的预测误差，发现所提模型在超短期预测准确性和稳定性方面具有显著优势；变量分析表明，组合使用多个气象变量显著优于单一变量，揭示了气象变量间复杂的交互和非线性关系。

Wind power generation is significantly influenced by environmental factors, exhibiting high randomness and volatility, making it difficult to accurately predict the amount of electricity generated. To address this, an ultra-short-term wind power forecasting model combining multi-head self-attention, convolutional neural network, and long short-term memory (AM-CNN-LSTM) is proposed. The multi-head self-attention mechanism assigns weights based on the varying importance of historical wind power and meteorological data, generating key feature representations and mitigating interference. The convolutional neural network extracts complex spatial patterns and local dependencies between wind power and meteorological factors. The long short-term memory network captures long-term dependencies and dynamic changes in time series, enhancing forecasting accuracy. The results demonstrate that under the optimal time window of T=4 h, the model achieves mean absolute error, root mean square error, and mean absolute percentage error of 13.45 %, 19.48 % and 3.78 %, respectively, It outperforms comparative methods such as LSTM, CNN and CNN-LSTM. A comparison of forecasting errors for the optimal time window of different models over the next eight sample points reveals that the proposed model offers significant advantages in ultra-short-term forecasting accuracy and stability. Variable analysis indicates that the combined use of multiple meteorological variables significantly outperforms single-variable, uncovering the complex interactions and nonlinear relationships among meteorological factors.