电子器件2024，Vol.47Issue(2)：448-457,10.DOI:10.3969/j.issn.1005-9490.2024.02.024
基于UMAP流形特征提取和KELM的非侵入式负荷监测方法研究
Research on Non-Intrusive Load Monitoring Method Based on Manifold Feature Extraction of UMAP and KELM
摘要
Abstract
Non-intrusive load monitoring is a key technology for smart data mining on the user side of the"strong smart grid".To address the problem of low accuracy of existing identification algorithms for superimposed state load,a non-intrusive load identification model based on the combination of uniform manifold approximation and projection(UMAP)and KELM is proposed.Firstly,UMAP is used to embed the original load features,extract the intra-class manifold structure of the load,and combine with stochastic gradient descent to optimize the global structure of the load,which effectively increases the distinguishability of the load features while retaining the original adjacent position information of the load.Then the kernel mapping network is constructed using radial basis functions,and the ACO algorithm is used to optimize the radial range of the mapping network and the penalty coefficients of the model to establish the optimal identification model.Compared with other machine learning-based identification methods,the proposed model achieves significant improvement in the identification accuracy of superimposed state load,reaching 98.48%and 99.44%on the TIPDM and BLUED datasets,respectively.关键词
非侵入式负荷监测/叠加态负荷/均匀流形逼近与投影/蚁群算法/核极限学习机Key words
non-intrusive load monitoring/superimposed state load/UMAP/ACO/KELM分类
信息技术与安全科学引用本文复制引用
张瀚文,李鹏,郎恂,沈鑫,梁俊宇,苗爱敏..基于UMAP流形特征提取和KELM的非侵入式负荷监测方法研究[J].电子器件,2024,47(2):448-457,10.基金项目
国家自然科学基金项目(62163036) （62163036）
云南省中青年学术和技术带头人后备人才培养计划项目(202105AC160094) （202105AC160094）
工业控制技术国家重点实验室开放课题项目(NoICT2022B24) （NoICT2022B24）
云南大学专业学位研究生实践创新项目(ZC-22222774) （ZC-22222774）
