Smart home energy management based on artificial emotion LSTM algorithm

doi:10.3969/j.issn.2097-0706.2023.11.004

Abstract

Abstract:

With the further deepening of China's social urbanization and the development of cities，there are shortages of social resources，especially power resources. Overload of electricity consumption forces power rationing in some districts to ease the pressure of municipal power supply. In this context，it is particularly important for users to manage and save energy autonomously. In order to enable users of household electricity to independently manage the usage of electric energy ， realize two-way interaction between the power grid and user ends，and ensure the implementation of demand-side response， a smart home energy management method based on artificial emotion long short-term memory （AELSTM）network algorithm is proposed. This method is mainly composed of artificial emotional deep neural network（AEDNN）and long-short-term memory（LSTM）network. The combination of the two components allows humanized real-time monitoring and management of household electricity consumption.

Key words: artificial emotion, Q learning, emotional deep neural network, long short-term memory network, energy forecasting, smart home energy management, intelligent grid, low carbon economy

CLC Number:

TK01⁺8:TP399

YIN Linfei, LIU Jinyuan. Smart home energy management based on artificial emotion LSTM algorithm[J]. Integrated Intelligent Energy, 2023, 45(11): 27-35.

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