基于GBDT算法的电力工程数据信息分析及预测方法研究  

Research on power engineering data information analysis and prediction method based on GBDT algorithm

作　　者：翁海兵 杨阳 黄颖 WENG Haibing;YANG Yang;HUANG Ying(Lishui Power Supply Company,State Grid Zhejiang Electric Power Co.,Ltd.,Lishui 323000,China)

机构地区：[1]国网浙江省电力有限公司丽水供电公司,浙江丽水323000

出　　处：《电子设计工程》2023年第24期154-158,共5页Electronic Design Engineering

基　　金：浙江丽水公司自动竣工决算辅助应用优化项目课题研究(ZJLS2021GFJJOP)。

摘　　要：在大数据分析技术不断完善的背景下,针对电力工程数据分析与预测仍存在精度较低的问题,提出了一种基于GBDT算法的电力工程数据信息分析及预测方法。该方法利用损失函数表征负梯度值,且在函数中增加了正则项,通过不断降低残差数值以增加数据的真实度,进而构建回归函数完成迭代循环操作,再将数据映射到新的特征空间。采取双向综合填补法来处理特征缺失的数据,并使用主成分分析法对电力工程数据特征加以提取,从而提高算法的预测精度。算例分析结果表明,当采用所提算法对电力工程数据进行分析与预测时,其预测精度较高,误差均在5%以内,具有一定的工程应用价值。Under the background of continuous improvement of big data analysis technology,aiming at the problem of low accuracy of power engineering data analysis and prediction,this paper proposes a power engineering data information analysis and prediction method based on GBDT algorithm.In this method,the loss function is used to represent the negative gradient value,and the regular term is added to the function.By continuously reducing the residual value to increase the authenticity of the data,the regression function is constructed to complete the iterative cycle operation,and then the data is mapped to a new feature space.The two⁃way comprehensive filling method is adopted to deal with the data with missing features,and the principal component analysis method is used to extract the features of power engineering data,so as to improve the prediction accuracy of the algorithm.The results of example analysis show that when the proposed algorithm is used to analyze and predict power engineering data,its prediction accuracy is high,and the error is less than 5%,which has a certain engineering application value.

关 键 词：GBDT算法 特征提取 数据融合 数据预测 

分 类 号：TP807[自动化与计算机技术—检测技术与自动化装置] TN99[自动化与计算机技术—控制科学与工程]