Aiming at the problem of low recognition accuracy for non-intrusive load identification method at the low sampling rate

a load identification method based on multi-feature sequence fusion is proposed. First

an integer programming model is developed to solve the possibility of load existence

thus reducing the dimensions of load identification process. According to the sliding window method

the combined power sequence and the original power sequence are obtained

from which the statistical characteristic value and the contour singular value are extracted. Then the probabilistic neural network(PNN)is used to obtain the observed sequence of the hidden Markov model(HMM). Meanwhile

the information of load sequence is fused by HMM

and the similarity between the observed sequence and the combined power sequence is calculated. Thus the load identification at a low sampling rate is completed and the power consumption of each household load is obtained. Finally

through the simulation experiments of single load identification

multi-load identification

load identification with different sampling rates and load identification of each user

the average results of the load accuracy and the identification accuracy are more than 85%

which has verified that the rationality and immediacy of the proposed method can meet the requirement of load identification at the low sampling rate.