A non-intrusive load decomposition method for residents

Abstract

Aiming at the problem that the large amount of data involved in the existing load decomposition method leads to low decomposition efficiency and high hardware requirements, a non-intrusive load decomposition method based on hidden Markov model (HMM) and improved Viterbi algorithm is proposed. Monitor steady state current of each load and quantify the data obtained and then establish the probability distribution of quantized monitoring values by statistics. The states that a load has can be identified from the probability distribution. The running states of each load at the same moment are arranged in sequence to form a combination state. HMM is created based on the correlation of combination states in the transition and the correlation between the combination state and the quantized monitoring value of the total current. Based on matrix sparsity, a compression algorithm is used to reduce data storage, and a query algorithm and an improved Viterbi algorithm are used to avoid unnecessary calculations about zero-probability terms to further improve the decomposition efficiency. Experimental results show that this method can effectively improve the decomposition efficiency and need lower hardware requirements while accurately obtaining the running state of various loads.