随着“双碳”目标的深入推进，近年来我国风电行业迅速发展，如何精准有效地预测风电功率对实现风机安全并网和维持系统稳定运行至关重要。针对现有风电功率预测方法存在输入特征冗余、泛化能力不足和未能充分捕捉风电出力内在特性等问题，提出了一种基于特征优选与相似相本融合的长短期记忆网络与注意力机制（long short term memory-attention memory，LSTM-AM）短期风电功率预测模型。首先，利用最小绝对收缩和选择算子（least absolute shrinkage and selection operator，Lasso）回归进行输入特征优选，减少冗余；然后，采用长短期记忆网络与注意力机制建立LSTM-AM融合网络模型；最后，通过欧氏距离计算提取相似历史样本，与模型输出加权作为最终预测值。实验结果表明，所提出的方法相比传统方法预测性能更优，在风电功率预测中表现出更高的准确性，能够为电力系统规划运行和可再生能源的深入应用提供支撑。

With the further promotion of the "Double Carbon" goal, China's wind power industry has developed rapidly in recent years. How to accurately and effectively predict wind power is crucial to realize the safe grid connection of wind turbines and maintain the stable operation of the system. Aiming at the problems such as input feature redundancy, insufficient generalization ability, and insufficient capture of inherent wind power output characteristics in existing wind power forecasting methods, a short-term wind power prediction model based on feature selection and similarity based fusion using long short term memory network-attention mechanism (LSTM-AM) is proposed. Firstly, least absolute shrinkage and selection operator (Lasso) regression is used to optimize input features to reduce redundancy. Then, the long short term memory network-attention mechanism（LSTM-AM） fusion network model is established using long short term memory and attetion mechanism. Finally, similar historical samples are extracted by Euclidean distance calculation and weighted with the model output as the final predicted value. The experimental results show that compared with traditional methods, the prediction accuracy of the proposed method is significantly improved, and it shows higher accuracy in wind power prediction, which can provide support for power system planning and operation and the in-depth application of renewable energy.