In view of the problems of insufficient accuracy and low computational efficiency of the current NILM technology in processing complex electrical loads and long time series

a TCN-1DEMA-MTL model is proposed. First

the model uses the TCN module to extract the contextual features of the data and realize multi-level feature fusion; then

the 1DEMA attention mechanism is introduced to dynamically weight and optimize the feature extraction effect; and the MTL framework is used to integrate the load decomposition and state recognition tasks to further improve the prediction accuracy. In the experimental stage

the proposed model is compared with the existing bidirectional LSTM

S2P model and LDwA model

and all performance indicators show significant advantages. The experimental results verify the effectiveness and practicality of the scheme

which provides a powerful reference for the development of residential power load monitoring.