Abstract
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.
Highlights
The introduction of smart grid technologies addresses most of the energy wastage problems by efficiently utilizing the advantages of communication technologies
Over time, smart grids based on traditional concepts and communication technologies exhibit many challenges such as inappropriate and delayed two-way communication between the grid and smart meter, pricing control based on demandresponse (DR) systems, difficulties in communication with renewable energy sources, unevenness load scheduling, and management, etc
To schedule the operation of the household appliances in smart homes, we propose a scheduling scheme for the operational time of smart home appliances based on reinforcement learning
Summary
The associate editor coordinating the review of this manuscript and approving it for publication was Sanjeevikumar Padmanaban. Turning of an electronic appliance A Boolean variable representing the operation of an electronic appliance n is either ON or OFF Operating power levels of an appliance Represents the state of an appliance n Represents the action performed by a Q-learning agent Represents the reward matrix The probability of interaction of a human i with an appliance n in a week A goal state i.e. performing all actions with less amount of energy. M. Khan et al.: Real-Time Scheduling of Operational Time for Smart Home Appliances max(E)
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