Non-intrusive load decomposition algorithm based on temporal convolutional network and long short-term memory model combined with strongly correlated power sequences

The deep learning neural network method is used to complete the non-intrusive load decomposition task, which often has the problems of poor model performance and low decomposition accuracy. In order to solve the problem that the decomposition accuracy is reduced because the model cannot fully learn all the power characteristics of a specific load in deep learning, a non-intrusive method based on the combination of TCN-LSTM two-layer neural network and strongly correlated power sequence is proposed. Firstly, the load decomposition algorithm learns the time-attribute power information mapping relationship through TCN, obtains the load state pre-identification result and coupling it with the prior data, obtains the independent strongly correlated power sequence of each electrical device to replace the original aggregate power sequence to improve the proportion of the target signal in the mixed signal, and inputs it into the LSTM to capture advanced time-domain information. The measured power sequence is used as a label to implement the neural network model. After training, the decomposed power sequences of five experimental loads were obtained and compared with the actual power sequences. In the part of case analysis, the UK-DALE public data set is used to verify the algorithm in the paper, and by comparing the algorithm in the paper with two typical algorithms, the effectiveness of the model in the paper is verified, and the high-precision load decomposition task is completed.