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 figure data is unavailable due to noise interference of stable operation data of household appliances of the actual measurement

a V-I map data screening algorithm is proposed

which screens the two-dimensional V-I feature map data of characterizing household appliances

improves the deep learning network

and achieves better recognition effect of household appliances by using transfer learning. The experimental result shows that not only the accuracy of the appliances identification is improved

but also the convergence speed of the algorithm is accelerated.