Event-Driven Non-Intrusive Load Monitoring Algorithm Based on Targeted Mining Multidimensional Load Characteristics

作     者：Gang Xie Hongpeng Wang Gang Xie;Hongpeng Wang

作者机构：School of Information and Communication EngineeringBeijing University of Posts and TelecommunicationsBeijing 100876China 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2023年第20卷第5期

页      面：40-56页

核心收录：

学科分类：080802[工学-电力系统及其自动化] 0808[工学-电气工程] 08[工学] 

基　　金：supported by National Natural Science Foundation of China(No.61531007) 

主　　题：non-intrusive load monitoring learning to ranking smart grid electrical characteristics 

摘      要：Nowadays,the advancement of nonintrusive load monitoring(NILM)has been hastened by the ever-increasing requirements for the reasonable use of electricity by users and demand side *** existing researches have tried their best to extract a wide variety of load features based on transient or steady state of electrical appliances,it is still very difficult for their algorithm to model the load decomposition problem of different electrical appliance types in a targeted manner to jointly mine their proposed *** paper presents a very effective event-driven NILM solution,which aims to separately model different appliance types to mine the unique characteristics of appliances from multi-dimensional features,so that all electrical appliances can achieve the best classification ***,we convert the multi-classification problem into a serial multiple binary classification problem through a pre-sort model to simplify the original ***,ConTrastive Loss K-Nearest Neighbour(CTLKNN)model with trainable weights is proposed to targeted mine appliance load *** simulation results show the effectiveness and stability of the proposed *** with existing algorithms,the proposed algorithm has improved the identification performance of all electrical appliance types.