A Non-intrusive Load monitoring Algorithm Based on Seq2point

Aiming at the problems of long training time and poor decomposition performance of neural network in the application of Sequence-to-point deep learning method in the field of non-intrusive load monitoring, a non-intrusive load monitoring optimization model based on Seq2point was proposed. In this model, the training speed of the neural network is optimized by the forward gated recurrent unite, and the decomposition performance is improved by the median filter and the standardized data pre-treatment method. Experimental results on public data set AMPDS2 show that this algorithm can effectively improve the accuracy of load decomposition and shorten the training time of network.