基于熵权法Stacking集成学习的多分类窃电检测  

Stacking Integrated Learning of Multi-classification ElectricityTheft Detection Based on Entropy Weight Method

作　　者：孙玉芹 王敏 田方 孙园 SUN Yu-qin;WANG Min;TIAN Fang;SUN Yuan(School of Mathematics and Physics,Shanghai Electric Power University,Shanghai 200070,China;School of Mathematics,Shanghai University of Finance and Economics,Shanghai 200433,China)

机构地区：[1]上海电力大学数理学院,上海200070 [2]上海财经大学数学学院,上海200433

出　　处：《科学技术与工程》2024年第30期12996-13004,共9页Science Technology and Engineering

基　　金：国家自然科学基金(12071274)。

摘　　要：为了精准定位窃电行为,减小电力窃取给电力系统带来的经济损失,提出了一种基于熵权法Stacking(stacking based entropy,E_Stacking)集成学习的多分类窃电检测模型。首先基于用电量信息共线性的特点,使用方差膨胀因子(variance inflation factor,VIF)作为标准对数据降维,以降低数据复杂度。然后在模型训练时嵌入k折交叉验证,有效防止模型过拟合。该模型包含初级学习器和元学习器两层学习器,可以充分结合两层学习器的优点,将学习的互补特征和判别特征相结合,进一步提高检测性能。最后,使用爱尔兰数据集和部分加州大学欧文分校(University of California Irvine,UCI)数据集验证模型,结果优于目前几种常见的方法,证明该模型的有效性和稳定性。To accurately locate electricity theft and reduce the economic losses caused by electricity theft to the power system,a multi-classification electricity theft detection model based on the entropy weight method Stacking E_Stacking(stacking based entropy)ensemble learning was proposed.First,based on the collinear characteristics of electricity consumption information,the VIF(variance inflation factor)was used as a standard to reduce the dimensionality of the data to reduce data complexity.Then k-fold cross-validation was embedded during model training to effectively prevent model overfitting.This model contained two layers of learners,a primary learner,and a meta-learner,it could fully combine the advantages of the two-layer learner and combine the learned complementary features and discriminative features to further improve detection performance.Finally,the Irish dataset and a portion of UCI(University of California Irvine)datasets were used to verify the model,and the results were better than several common methods at present,which demonstrates that the model has certain effectiveness and robustness.

关 键 词：熵权法 STACKING 集成学习 多分类 窃电检测 方差膨胀因子 

分 类 号：TM711[电气工程—电力系统及自动化]