计算机应用与软件2025，Vol.42Issue(5)：62-71,10.DOI:10.3969/j.issn.1000-386x.2025.05.010
基于稀疏自编码器SAE和优化RUSBoost的窃电检测
ELECTRICITY THEFT DETECTION BASED ON SPARSE AUTOENCODER AND OPTIMIZED RUSBOOST
摘要
Abstract
In order to improve the detection accuracy and reduce the computational complexity,a electricity theft detection based on sparse autoencoder(SAE)and optimized RUSBoost is proposed.According to the three aspects of the relationship between users,temperature and power consumption,the electricity users were marked as benign or malicious users.After assigning labels to the data,features were extracted from the data by introducing reconstruction based independent component analysis and SAE.Differential evolution random under sampling enhanced RUSBoost and Jaya optimized RUSBoost were used for classification.The experimental results of the last two data sets show that the proposed method can achieve low complexity and high-precision electricity theft detection.关键词
重构独立成分分析/稀疏自动编码器/窃电检测/差分进化Key words
Reconstruction independent component analysis/Sparse autoencoder/Electricity theft detection/Differ-ential evolution分类
信息技术与安全科学引用本文复制引用
袁铭敏,姚鹏,易欣,曾纬和,李乾,孙健..基于稀疏自编码器SAE和优化RUSBoost的窃电检测[J].计算机应用与软件,2025,42(5):62-71,10.基金项目
国家电网公司总部科技项目(520201150012). （520201150012）
