Non-intrusive Load Identification Algorithm Based on Convolutional Attention
Aiming at the problems of low accuracy and long training time of traditional non-invasive load monitoring algorithm,this paper proposes a non-intrusive load identification algorithm based on convolutional attention.Firstly,the minimum running and stopping time are set for the load data to reduce the interference caused by measurement errors.Then the convolutional neural network is used to train the load data.The constructed neural network includes encoder,time pool converter and decoder,and the convolutional attention module is introduced into the decoder to calculate the most important information at the current moment in the time series.Finally,the proposed load identification model is verified by using the UKDALE dataset,and compared with the existing algorithms.Simulation results show that the pro-posed algorithm has better identification accuracy and generalization ability,and the training time is reduced by about 27.9％.
NETL
NSTL
万方数据
维普
