A networked smart home system based on recurrent neural networks and reinforcement learning. Issue 1 (1st January 2021)

• Record Type: Journal Article
• Authors: Li, Zhongwang; Deng, Bin
• Is Part Of: Systems science & control engineering. Volume 9:Issue 1(2021)
• Page Start: 775
• Page End: 783
• Publication Date: 2021-01-01
• Subjects: Smart home -- swarm intelligent decision-making -- recurrent neural network -- reinforcement learning; System theory -- Periodicals; Automatic control -- Periodicals; 003.05
• DOI: 10.1080/21642583.2021.2001769
• Languages: English
• ISSNs: 2164-2583

Abstract

Abstract : With the widespread application of smart home systems, the optimal design of smart home systems has received considerable research attention. This paper puts forward a network smart home system design scheme based on the analysis of the indoor environment and the forecast of the future indoor environment. By building a multi-level network model, an integrated model system from analysis, prediction to decision-making is formed. The swarm intelligent decision-making ability of the networked smart home system is realized by applying a recurrent neural network and a reinforcement learning method. Meanwhile, the indoor simulation environment is built, the indoor environment variables are simulated and the performance of the system is verified by the simulation environment. The simulation results show that the networked smart home system has advantages over the single smart home equipment in the performance of indoor comfort improvement.