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The overall system architecture is shown in the following figure.
The structure of the system is environment and agent.
Environment is divided into getting data, EV data, home data, and steps.
Get data collects data.
Data on PV's power output and requirements are collected from the Smartstar project.
This is managed by the home database after importing data in CSV form.
Data related to EVs are collected by the ACN dataset.
This is managed by EV Dataset by collecting data in the form of HTTP requests and parsing JSON format data.
The step is the unit of change in the environment.
It takes data from EV dataset, one dataset.
It also receives an action from the agent, updates the state, and calculates return and done.
Each state, reward, and done is delivered to the agent. Agents are divided into initiation, test agent, and learning agent.
initiation initializes the agent.
In this paper, we use DQN.
The learning agent uses the epsilon greedy search method as an agent for learning.
The test agent uses the greedy search method as an agent to check the learned results.
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IEEE/ICACT20220153 Slide.06
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