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Non-intrusive load monitoring and decomposition method based on decision tree
Journal of Mathematics in Industry Pub Date : 2020-01-10 , DOI: 10.1186/s13362-020-0069-4
Jiang Lin , Xianfeng Ding , Dan Qu , Hongyan Li

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.

中文翻译:

基于决策树的非侵入式负荷监测与分解方法

为了从负荷识别和负荷分解两个方面实现非侵入式负荷监测与分解(NILMD)的问题,基于数据库的负荷特征,本文首先对混合电气设备组的设备组成进行了分析和识别。通过使用负载决策树算法。然后,建立设备状态识别的0-1编程模型,并使用粒子群优化(PSO)求解设备状态识别模型,并确定设备组中的设备运行状态。最后,对第六届“泰迪杯”数据挖掘挑战赛中问题A的部分数据进行了仿真实验。
更新日期:2020-01-10
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