电力需求侧管理2024，Vol.26Issue(3)：55-61,7.DOI:10.3969/j.issn.1009-1831.2024.03.009
基于最优特征和改进随机森林的非侵入式负荷辨识方法
Non-intrusive load identification method based on optimal signatures and improved random forest
摘要
Abstract
Non-intrusive load monitoring method is a critical technology for realizing power system intelligence,which helps to optimize en-ergy management and promote efficient energy utilization.In order to cope with the existing problems of feature redundancy,limited recog-nition accuracy and computational inefficiency,a novel non-intrusive load identification method based on optimal signatures and improved random forest is proposed.Firstly,the optimal feature combination is autonomously determined by recursive feature elimination method to reduce the information redundancy.Then load identification is realized by constructing a weighted random forest model.The weights are established by utilizing out-of-bag data.Construction parameters of random forest are optimized using the improved whale algorithm.Ulti-mately,the experimental results prove the accuracy and superiority of the proposed load identification method.关键词
非侵入式负荷监测/负荷辨识/随机森林/递归特征消除/鲸鱼算法Key words
non-intrusive load monitoring/load identification/random forest/recursive feature elimination/whale optimization algorithm分类
信息技术与安全科学引用本文复制引用
李利刚,刘浩,陈建强,王昊川,罗世超,高源,李凤朝..基于最优特征和改进随机森林的非侵入式负荷辨识方法[J].电力需求侧管理,2024,26(3):55-61,7.基金项目
陕西省自然科学基础研究计划资助项目(2023-JC-YB-335) （2023-JC-YB-335）
