电测与仪表2025，Vol.62Issue(11)：53-60,8.DOI:10.19753/j.issn1001-1390.2025.11.006
基于ITSO-XGBoost算法的智能电能表误差估计模型研究
Research on error estimation model of smart electricity meter based on ITSO-XGBoost algorithm
摘要
Abstract
Traditional methods have limitations such as difficulty in adapting to complex and changeable environ-ments,poor sensitivity and high complexity when dealing with practical problems.To achieve the goal of remotely monitoring the status of electricity meters and timely and accurately detecting abnormalities of electricity meters,the ITSO-XGBoost algorithm is innovatively introduced in this paper.By combining the improved teaching optimization algorithm(ITSO)and the extreme gradient boosting algorithm(XGBoost),a new error estimation method for elec-tricity meters is proposed.A method for predicting the error of the calibrator under complex field conditions by using XGBoost is proposed.In order to balance the exploration and development capabilities of the algorithm,an Aquila Eagle hybrid tuna swarm optimization algorithm is proposed to improve the XGBoost algorithm and enhance its competi-tiveness.The results of multiple sets of experiments show that the ITSO-XGBoost algorithm performs well in the error es-timation of electricity meters,not only significantly higher than the traditional machine learning algorithms in terms of prediction accuracy.Furthermore,this algorithm has a relatively low sensitivity to feature selection and can maintain sta-ble performance when dealing with high-dimensional data,demonstrating strong robustness and adaptability.关键词
ITSO-XGBoost/电能表/误差估计/模型Key words
ITSO-XGBoost/electricity meter/error estimation/model分类
动力与电气工程引用本文复制引用
金耀..基于ITSO-XGBoost算法的智能电能表误差估计模型研究[J].电测与仪表,2025,62(11):53-60,8.基金项目
国网安徽省电力公司科技项目(SGAHXT00TKJS2000033) （SGAHXT00TKJS2000033）
