Journal
SENSORS
Volume 20, Issue 12, Pages -Publisher
MDPI
DOI: 10.3390/s20123450
Keywords
reinforcement learning; home energy management; appliance scheduling; human-appliance interaction; user comfort
Funding
- National Research Foundation of Korea(NRF) - Korea government(MSIT) [2019R1F1A1042721]
- BK21 Plus project (SW Human Resource Development Program for Supporting Smart Life) - Ministry of Education, School of Computer Science and Engineering, Kyungpook National University, Korea [21A20131600005]
- National Research Foundation of Korea [2019R1F1A1042721] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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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.
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