电网技术2024，Vol.48Issue(5)：2095-2102,中插48-中插55,16.DOI:10.13335/j.1000-3673.pst.2023.0479
基于加权宽度学习的异常用电辨识研究
Research on Abnormal Electricity Identification Based on Weighted Broad Learning System
摘要
Abstract
Aiming at the problem of unbalanced relationship between abnormal electricity consumption and normal electricity consumption sample categories,time-consuming training and lack of scalability of existing models,an abnormal electricity consumption identification model based on Weighted Broad Learning System(WBLS)was proposed.Firstly,considering the class imbalance relationship between samples,the sample weight is used in the objective function to constrain the contribution of each class to the model,and the sample weight is personalized according to the distribution of samples,and the generalized inverse WBLS identification model is established efficiently by ridge regression.Secondly,based on the newly added electricity consumption sample data,the model is updated and reconstructed by the incremental learning algorithm.The experimental results show that the model improves the identification accuracy of abnormal electricity samples,and can quickly update and expand the old model with the increase of electricity samples.关键词
异常用电/加权宽度学习/类不平衡/增量学习Key words
abnormal power consumption/weighted broad learning system/class imbalance/incremental learning分类
信息技术与安全科学引用本文复制引用
姚影,陆俊,肖琦,龚钢军,徐志强,辛培哲..基于加权宽度学习的异常用电辨识研究[J].电网技术,2024,48(5):2095-2102,中插48-中插55,16.基金项目
国家电网有限公司总部管理科技项目(5700-2-2252219 A-1-1-ZN).Project Supported by the Headquarters of State Grid Corporation of China Manages Scientific and Technological Projects(5700-2-2252219 A-1-1-ZN). （5700-2-2252219 A-1-1-ZN）
