科技创新与应用2024，Vol.14Issue(7)：28-34,7.DOI:10.19981/j.CN23-1581/G3.2024.07.008
一种基于信息熵的LSTM时间序列数据预测模型
摘要
Abstract
Time series prediction can improve the effectiveness of smart grid decision-making energy consumption evaluation and the fault detection efficiency of power sensor networks.Based on Shannon information entropy and long short-term memory network,a trend prediction model based on time series data is constructed.Firstly,the model algorithm merges the features of time series data with entropy method,and establishes the feature interval and entropy model.Secondly,on the basis of the establishment of the feature interval,the classified data are trained in the long-term memory network to get the prediction results.Finally,the experimental results show that,compared with the traditional LSTM and GRU models,the iterative error of the mean square variance function of the high entropy model is reduced by 85.9%and 85.29%,which significantly improves the reliability and accuracy of the model prediction results.关键词
智能电网/时间序列/信息熵/长短期记忆神经网络/预测模型Key words
smart grid/time series/information entropy/long-term and short-term memory neural network/prediction model分类
信息技术与安全科学引用本文复制引用
田园,孙梦觉,周植高,范培忠..一种基于信息熵的LSTM时间序列数据预测模型[J].科技创新与应用,2024,14(7):28-34,7.基金项目
云南电网有限责任公司信息中心研发基金(059300202021030302YY00012) （059300202021030302YY00012）
