Load monitoring is an important part of intelligent electricity consumption. For the non-intrusive load monitoring

a deep neural network based non-intrusive load disaggregation method is proposed. Firstly

a modified iterative appliance state clustering algorithm is proposed. By modifying the stopping criteria and adding eliminating criteria of redundant clusters

the clustering results are more consistent with the actual appliance operation. An appliance time characteristic model is proposed considering weak time characteristics of hidden Markov models which are commonly used in the current study. The appliance characteristics and user habits are taken into consideration. The electrical appliances are modeled from the perspective of time. A deep neural network is constructed to perform load disaggregation. The input of the network includes appliance states

power and time information. The history data and the generated data based on models are used to train the network parameters. The effectiveness and accuracy of the method are verified on the data set.