Abstract
This paper designs and develops a real-time charger management system which keeps collecting the status information from chargers and converts control messages from the total operation center to a command predefined for charger control. RF cards complete the service chain from electric vehicles to the operation center, making the information flow bidirectional. The extended coverage of the operation control over the charging infrastructure allows an easy payment for the charging fee, based on the membership management and personalized services. A web application is implemented on the digital map of the target city for users to retrieve necessary information from the system and find the best service and chargers. The massive amount of real-time charger monitoring data is being accumulated in the database, and big data analysis will allow us to make intelligent plans for future smart grid city services
Junghoon Lee ,Gyung-Leen Park
Dept. of Computer Science and Statistics, Jeju National University
The time series of currently 78 days is converted to a set of learning patterns of a neural network model. The FANN library defines the text file format for learning patterns and trace sequences. Based on this forecast model, we trace the previous behaviors of the number of daily records. Here, the solid line is the traced utilization pattern, and it finds the overall trend in the time series. It is expected that the forecast model will be improved with more data.
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This paper designs and develops a real-time charger management system which keeps collecting the status information from chargers and converts control messages from the total operation center to a command predefined for charger control. RF cards complete the service chain from electric vehicles to the operation center, making the information flow bidirectional. The extended coverage of the operation control over the charging infrastructure allows an easy payment for the charging fee, based on the membership management and personalized services. A web application is implemented on the digital map of the target city for users to retrieve necessary information from the system and find the best service and chargers. The massive amount of real-time charger monitoring data is being accumulated in the database, and big data analysis will allow us to make intelligent plans for future smart grid city services
Junghoon Lee ,Gyung-Leen Park
Dept. of Computer Science and Statistics, Jeju National University
The time series of currently 78 days is converted to a set of learning patterns of a neural network model. The FANN library defines the text file format for learning patterns and trace sequences. Based on this forecast model, we trace the previous behaviors of the number of daily records. Here, the solid line is the traced utilization pattern, and it finds the overall trend in the time series. It is expected that the forecast model will be improved with more data.
Download
