In demand response programs, load service entity (LSE) can aggregate user as an independent entity to participate in the day ahead energy market, and complete the response target through incentive in the next day. In order to reduce the incentive cost of LSE, a differentiated incentive mechanism that consider the differences in user response flexibility is proposed in this paper. Then, a user response behavior model is established by using the long short-term memory (LSTM) network, with aim of

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... ccurately predicting users' response. Subsequently, an optimization strategy combining particle swarm (PSO) and LSTM is proposed, so that the response target can be accurately completed with low cost. Simulation experiments verified that the cost of LSE is close to the theoretical minimum, and can be reduced by 20% compared with the optimal result under unified incentive mechanism. Moreover, it also verified that the proposed strategy has high response accuracy and good stability. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/ VOLUME 8, 2020 YI SUN was born in Liaoning, China, in 1972. He received the M.S. degree in communication and information system and the Ph.D. degree in electric information technology from North