融合Attention与改进LSTM的电力工程数据分析算法设计

Design of power engineering data analysis algorithm integrating Attention and improved LSTM

针对传统数据分析算法处理高维、海量数据过程中出现的低效、准确率差的问题,基于改进LSTM提出了一种电力工程数据分析与预测算法。该算法使用双向LSTM作为训练模型,从而捕捉到更为广泛的上下文信息。对于模型处理高维数据时遇到数据冗余、噪声较高的问题,使用堆叠稀疏自编码器进行输入数据预处理,进而提升了模型的泛化能力,在模型输出部分结合自注意力机制,进一步聚焦关键特征,提高模型在不同序列集中的性能表现。实验结果表明,在所有对比算法中,文中所提算法的误差最低且性能最优,实际数据误差小于5%,满足电力工程的实际应用需求。

Aiming at the problems of inefficiency and poor accuracy in processing high-dimensional and massive data using traditional data analysis algorithms,a power engineering data analysis and prediction algorithm based on improved LSTM is proposed.The algorithm uses bidirectional LSTM as a training model to capture broader contextual information.For models that encounter data redundancy and high noise when processing high-dimensional data,stacked sparse self encoders are used to preprocess input data,thereby improving the generalization ability of the model.Combining self attention mechanisms in the model output section,key features are further focused to improve the performance of the model in different sequence sets.The experimental results show that among all the comparison algorithms,the proposed algorithm has the lowest error,the best performance,and the actual data error is less than 5%,meeting the practical application requirements of power engineering.