Open AccessResearch on Non-intrusive Load Decomposition Based on FHMM
Author(s) -
Chunhui Yang,
Zhensheng Wu
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/768/6/062046
Subject(s) - correctness , computer science , viterbi algorithm , decomposition , hidden markov model , state (computer science) , markov chain , decomposition method (queueing theory) , algorithm , artificial intelligence , mathematics , machine learning , statistics , ecology , biology
Non-intrusive load monitoring and decomposition, as one of the important parts of intelligent power utilization system, can deeply analyze users’ internal load components and obtain user’s electricity consumption information from different scales, which is of great significance to users and power companies. In this paper, a non-intrusive load decomposition method based on factorial hidden Markov model using low frequency data is proposed. Kmeans-II algorithm is used to cluster the working state of a single load, the results of which are used to calculate the parameters of the HMM for individual load model. The total load model is represented by a factorial hidden Markov model, which transforms the load decomposition into an optimization problem with maximum probability. The improved Viterbi algorithm based on event detection is proposed to solve this optimization problem, so as to obtain the working state sequence and realize load decomposition. Finally, the correctness and practicability of the method are verified by an example.