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• W3036617008 abstract "Maintaining a fair use of energy consumption in smart homes with many household appliances requires sophisticated algorithms working together in real time. Similarly, choosing a proper schedule for appliances operation can be used to reduce inappropriate energy consumption. However, scheduling appliances always depend on the behavior of a smart home user. Thus, modeling human interaction with appliances is needed to design an efficient scheduling algorithm with real-time support. In this regard, we propose a scheduling algorithm based on human appliances interaction in smart homes using reinforcement learning (RL). The proposed scheduling algorithm divides the entire day into various states. In each state, the agents attached to household appliances perform various actions to obtain the highest reward. To adjust the discomfort which arises due to performing inappropriate action, the household appliances are categorized into three groups i.e., (1) adoptable, (2) un-adoptable, (3) manageable. Finally, the proposed system is tested for the energy consumption and discomfort level of the home user against our previous scheduling algorithm based on least slack time phenomenon. The proposed scheme outperforms the Least Slack Time (LST) based scheduling in context of energy consumption and discomfort level of the home user." @default.
• W3036617008 created "2020-06-25" @default.
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• W3036617008 date "2020-06-18" @default.
• W3036617008 modified "2023-09-26" @default.
• W3036617008 title "A Multi-Objective Approach for Optimal Energy Management in Smart Home Using the Reinforcement Learning" @default.
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• W3036617008 doi "https://doi.org/10.3390/s20123450" @default.
• W3036617008 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7349083" @default.
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