Abstract:

Aiming at the problem that the accuracy of traditional non-intrusive load identification algorithms is low in low-frequency sampling data,a non-intrusive load identification algorithm based on the gram angle field (GAF) and feature fusion is proposed.The collected power information is converted into color image features by GAF technology to improve the identification.The image and power data are input into the convolutional neural network and the backpropagation neural network respectively for feature extraction to achieve feature fusion,which is used as the new feature for identification.The identification method is verified in many aspects of the public data set and compared with different classification algorithms.The results show that the image features carry more information,strengthen the representation of features,and feature fusion can solve the problem of information loss in the process of image coding,and improve the load identification ability of the model.

Key words: non-intrusive load identification, gramian angular field (GAF), feature fusion, deep learning