Real-Time Scheduling of Operational Time for Smart Home Appliances Based on Reinforcement Learning
The scheduling of the operational time of household appliances requires several parameters to be tuned according to the available energy supplied to a smart home. However, scheduling of operational time of multiple appliances in a smart home itself is the NP-hard problem and thus requires an intelli...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9122531/ |
| _version_ | 1818929456322969600 |
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| author | Murad Khan Junho Seo Dongkyun Kim |
| author_facet | Murad Khan Junho Seo Dongkyun Kim |
| author_sort | Murad Khan |
| collection | DOAJ |
| description | The scheduling of the operational time of household appliances requires several parameters to be tuned according to the available energy supplied to a smart home. However, scheduling of operational time of multiple appliances in a smart home itself is the NP-hard problem and thus requires an intelligent, heuristic method to be solved in polynomial time. In this research work, we propose Real-time Scheduling of Operational Time of Household Appliances based on the well-known value iterative reinforcement learning called Quality learning (RSOTHA-QL). The proposed RSOTHA-QL scheme operates in two phases. In the first phase, the agents of the Q learning act by interacting with the smart home environment and obtain a reward. The reward value is further utilized to schedule the operational time of household appliances in the next state ensuring minimum energy consumption. In the second phase, the dissatisfaction arises due to scheduling of operational time of the household appliances of the home user is maintained by categorizing the household appliances into three groups: 1) deferrable, 2) non-deferrable, and 3) controllable. Besides, using the shared memory synchronization phenomenon, the agents attached to each appliance of the smart home become coordinated. The simulation and experiments are performed in a smart home scenario comprised of a single user and multiple appliances. As compared with our previous research work using the Least Slack Time (LST) scheduling algorithm and scheduling based on demand-response strategy, it is revealed that the operational time of the household appliances is efficiently scheduled to reduce the energy consumption and dissatisfaction level of the home users significantly. |
| first_indexed | 2024-12-20T03:45:05Z |
| format | Article |
| id | doaj.art-ee80ae582825407ba6b08c1599002c83 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T03:45:05Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ee80ae582825407ba6b08c1599002c832022-12-21T19:54:38ZengIEEEIEEE Access2169-35362020-01-01811652011653410.1109/ACCESS.2020.30041519122531Real-Time Scheduling of Operational Time for Smart Home Appliances Based on Reinforcement LearningMurad Khan0https://orcid.org/0000-0001-9905-8904Junho Seo1Dongkyun Kim2School of Computer Science and Engineering, Kyungpook National University, Daegu, South KoreaSchool of Computer Science and Engineering, Kyungpook National University, Daegu, South KoreaSchool of Computer Science and Engineering, Kyungpook National University, Daegu, South KoreaThe scheduling of the operational time of household appliances requires several parameters to be tuned according to the available energy supplied to a smart home. However, scheduling of operational time of multiple appliances in a smart home itself is the NP-hard problem and thus requires an intelligent, heuristic method to be solved in polynomial time. In this research work, we propose Real-time Scheduling of Operational Time of Household Appliances based on the well-known value iterative reinforcement learning called Quality learning (RSOTHA-QL). The proposed RSOTHA-QL scheme operates in two phases. In the first phase, the agents of the Q learning act by interacting with the smart home environment and obtain a reward. The reward value is further utilized to schedule the operational time of household appliances in the next state ensuring minimum energy consumption. In the second phase, the dissatisfaction arises due to scheduling of operational time of the household appliances of the home user is maintained by categorizing the household appliances into three groups: 1) deferrable, 2) non-deferrable, and 3) controllable. Besides, using the shared memory synchronization phenomenon, the agents attached to each appliance of the smart home become coordinated. The simulation and experiments are performed in a smart home scenario comprised of a single user and multiple appliances. As compared with our previous research work using the Least Slack Time (LST) scheduling algorithm and scheduling based on demand-response strategy, it is revealed that the operational time of the household appliances is efficiently scheduled to reduce the energy consumption and dissatisfaction level of the home users significantly.https://ieeexplore.ieee.org/document/9122531/Energy consumptionQ~learningschedulingsmart home |
| spellingShingle | Murad Khan Junho Seo Dongkyun Kim Real-Time Scheduling of Operational Time for Smart Home Appliances Based on Reinforcement Learning IEEE Access Energy consumption Q~learning scheduling smart home |
| title | Real-Time Scheduling of Operational Time for Smart Home Appliances Based on Reinforcement Learning |
| title_full | Real-Time Scheduling of Operational Time for Smart Home Appliances Based on Reinforcement Learning |
| title_fullStr | Real-Time Scheduling of Operational Time for Smart Home Appliances Based on Reinforcement Learning |
| title_full_unstemmed | Real-Time Scheduling of Operational Time for Smart Home Appliances Based on Reinforcement Learning |
| title_short | Real-Time Scheduling of Operational Time for Smart Home Appliances Based on Reinforcement Learning |
| title_sort | real time scheduling of operational time for smart home appliances based on reinforcement learning |
| topic | Energy consumption Q~learning scheduling smart home |
| url | https://ieeexplore.ieee.org/document/9122531/ |
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