LAUSR

Abstract The Non-intrusive household load indentification can realize a series of power quality analysis such as power management, energy monitoring. It has the advantages of low cost, easy implementation. Aiming at the problem that a large number of V-I trajectory sample data is unavailable due to noise interference of stable operation data of household appliances of the actual measurement, a V-I map sample data set screening algorithm is proposed, which screen the two-dimensional V-I feature map data sets of characterizing household appliances, improves the deep learning network, and achieves better recognition effect of household appliances by using transfer learning. Experiments show that this method can effectively improve the accuracy of load identification algorithm, and has more advantages than traditional methods.