Non-intrusive load monitoring and decomposition method based on decision tree

被引：6
|
作者：
Lin, Jiang [1 ]
Ding, Xianfeng [1 ]
Qu, Dan [2 ]
Li, Hongyan [1 ]
机构：
[1] Southwest Petr Univ, Sch Sci, Chengdu, Peoples R China
[2] Sichuan Univ Sci & Engn, Sch Math & Stat, Zigong, Peoples R China
来源：
JOURNAL OF MATHEMATICS IN INDUSTRY | 2020年 / 10卷 / 01期
关键词：
Non-intrusive load detection; Load characteristics; Decision tree identification; 0-1 programming model; Particle Swarm Optimization (PSO);
D O I：
10.1186/s13362-020-0069-4
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
In order to realize the problems of non-intrusive load monitoring and decomposition (NILMD) from two aspects of load identification and load decomposition, based on the load characteristics of the database, this paper firstly analyzes and identifies the equipment composition of mixed electrical equipment group by using the load decision tree algorithm. Then, a 0-1 programming model for the equipment status identification is established, and the Particle Swarm Optimization (PSO) is used to solve the model for equipment state recognition, and the equipment operating state in the equipment group is identified. Finally, a simulation experiment is carried out for the partial data of Question A in the 6th "teddy cup" data mining challenge competition.
引用
收藏
页数：14
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