Based on the monitoring data of household smart meters

a sample database of residents’demand response is constructed. Based on the sample database

feature engineering is carried out to fully mine the characteristics of responsive users

such as family attributes

response behavior and power consumption behavior. On this basis

the self-learning optimization model of resident power demand response neural network is constructed. According to the data of different family labels and historical response results

the participation of resident demand response is predicted. With the continuous development of demand response in typical scenarios

the model is iterated and optimized. Finally

according to the regulation objectives

the demand response regulation strategy is intelligently formulated. Results show that the proposed demand response strategy can accurately identify the residential demand response participation and reduce the demand response incentive cost.