Non-intrusive Load Decomposition Method based on the Factor Hidden Markov Model

被引:0
|
作者
Liu Song [1 ,2 ]
Wu Yao [1 ,2 ]
Tian Jie [1 ,2 ]
机构
[1] North China Elect Power Univ, Yangzhong Intelligent Elect Inst, Beijing 212200, Jiangsu, Peoples R China
[2] North China Elect Power Univ, Coll Elect & Elect Engn, Beijing 102206, Peoples R China
关键词
Non-intrusive load decomposition; Factor hidden markov model; Steady-state characteristics; Smart meter;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Aiming at the problems of demanding high frequency sampling devices and the factor hidden markov model algorithm without considering the correlation between the power equipments' running states, a novel non-intrusive load monitoring and decomposition (NILMD) method which based on factor hidden markov mode is proposed in this paper. This method considers appliances' current as load feature, and establishes a mathematical model between the total current and the currents of each appliance that considers the correlation between the running states of the equipment. Then, factor hidden markov algorithm is applied to search the running states of each appliance that realizes the decomposition of power load. The experiment shows that the resolution precision of each electric equipment is improved, and the principle of the method is simple. Meanwhile, the load data required in this method can be obtained directly by the universal smart meters on the market which reduces the cost input of the hardware.
引用
收藏
页码:8994 / 8999
页数:6
相关论文
共 50 条
  • [41] Non-intrusive Load Decomposition of Public Buildings Based on Deep Learning and Transfer Learning
    Yang, Xiu
    Wu, Jihai
    Sun, Gaiping
    Tian, Yingjie
    Wang, Haojing
    Li, An
    [J]. Dianwang Jishu/Power System Technology, 2022, 46 (03): : 1160 - 1168
  • [42] Research on Non-intrusive Load Decomposition Based on Improved AP Clustering and Optimized GRNN
    Wang, Fanrong
    Xiang, Kun
    Liu, Hui
    [J]. Gongcheng Kexue Yu Jishu/Advanced Engineering Sciences, 2020, 52 (04): : 56 - 65
  • [43] Elimination of Overfitting of Non-intrusive Load Monitoring Model
    Zhou, Yongjun
    Ji, Chao
    Dong, Zhihua
    Yang, Lin
    Zhang, Shu
    [J]. 2021 IEEE IAS INDUSTRIAL AND COMMERCIAL POWER SYSTEM ASIA (IEEE I&CPS ASIA 2021), 2021, : 1567 - 1571
  • [44] Non-Intrusive Load Identification Method Based on Self-Supervised Regularization
    Zhao, Ruifeng
    Lu, Jiangang
    Liu, Bo
    Yu, Zhiwen
    Ren, Yanru
    Zheng, Wenjie
    [J]. IEEE ACCESS, 2023, 11 : 144696 - 144704
  • [45] Non-Intrusive Load Monitoring Based on the Graph Least Squares Reconstruction Method
    Ma, Xiaoyang
    Zheng Diwen
    Ying, Wang
    Yang, Wang
    Hong, Luo
    [J]. IEEE TRANSACTIONS ON POWER DELIVERY, 2022, 37 (04) : 2562 - 2570
  • [46] Hollow village detection method based on non-intrusive power load monitoring
    Liu, Rui
    Wang, Donglai
    Chen, Yan
    Guo, Rui
    Shi, Jiaqi
    [J]. ENERGY REPORTS, 2023, 9 : 407 - 415
  • [47] Non-intrusive Load Disaggregation Method Based on Edge Embedded Deep Learning
    Liu, Yaoxian
    Sun, Yi
    Li, Bin
    Huang, Ting
    [J]. Dianwang Jishu/Power System Technology, 2019, 43 (12): : 4329 - 4336
  • [48] Non-intrusive Load Monitoring Method Based on Improved Differential Evolution Algorithm
    Lu, Chunguang
    Ma, Lvbin
    Xu, Tao
    Ding, Guofeng
    Wu, Chenghuan
    Jiang, Xuedong
    [J]. 2019 11TH INTERNATIONAL CONFERENCE ON MEASURING TECHNOLOGY AND MECHATRONICS AUTOMATION (ICMTMA 2019), 2019, : 279 - 283
  • [49] Research on Non-Intrusive Load Monitoring Method Based on Feature Difference Enhancement
    Wang Min
    Zhou Yidi
    Zhang Shuang
    Zheng Yaopeng
    Liu Zihan
    Feng Hui
    [J]. INTEGRATED FERROELECTRICS, 2020, 210 (01) : 127 - 139
  • [50] Hollow village detection method based on non-intrusive power load monitoring
    Liu, Rui
    Wang, Donglai
    Chen, Yan
    Guo, Rui
    Shi, Jiaqi
    [J]. ENERGY REPORTS, 2023, 9 : 407 - 415