Non-intrusive load monitoring method based on v—i trajectory and neural network

Abstract

A non-intrusive load monitoring method based on V-I trajectory and neural network includes: collecting the household voltage, current and active power data in real time; determining whether there is a switching event and whether the load operating state has reached a steady state through the change of the active power; obtaining the voltage, current and power data of the load, converting the V-I trajectory into RGB color image containing the phase difference between the voltage and current, power and other information. After obtaining the RGB color image, performing normalization processing and performing load monitoring through pre-trained convolutional neural network. The present disclosure fully extracts the steady-state feature of the load through the convolutional neural network, and the neural network model can directly run on an embedded device, and does not need to rely on the computing support of a server.