Research on Big Data Mining Technology of Electric Vehicle Charging Behaviour
Thousands of electric vehicles (EV), which are large in number and flexible in their use of electricity, will be connected to the power system in the near future, which will bring more uncertainty to the power system. Therefore, it is necessary to study the general characteristics of EV charging beh...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Kaunas University of Technology
2019-12-01
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| Series: | Elektronika ir Elektrotechnika |
| Subjects: | |
| Online Access: | http://eejournal.ktu.lt/index.php/elt/article/view/24827 |
| _version_ | 1828809265952849920 |
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| author | Yujun Liu Yi Hong Cheng Hu |
| author_facet | Yujun Liu Yi Hong Cheng Hu |
| author_sort | Yujun Liu |
| collection | DOAJ |
| description | Thousands of electric vehicles (EV), which are large in number and flexible in their use of electricity, will be connected to the power system in the near future, which will bring more uncertainty to the power system. Therefore, it is necessary to study the general characteristics of EV charging behaviours. In the charging process, big data regarding charging behaviour of EVs are generated. This paper proposes a big data mining technique based on Random Forest and Principle Component Analysis for EV charging behaviour to identify and analyse clusters with different charging characteristics from the big data. This paper uses Dundee’s January 2018 EV charging data to conduct experiments, and obtains the charging behaviour clusters of the workdays, weekends, and holidays of January. The superiority of the random forest algorithm in the EV clustering problem is reflected when compared to the Euclidean distance method. The clusters obtained by the random forest algorithm have clearer characteristics, including the user’s charging method and travel behaviour. The results show that the charging behaviour of EVs has certain regularity, and the charging load has obvious peak-to-valley difference that is necessary to be regulated. |
| first_indexed | 2024-12-12T08:54:04Z |
| format | Article |
| id | doaj.art-8e841acdc4d042c8a3892171567a9d19 |
| institution | Directory Open Access Journal |
| issn | 1392-1215 2029-5731 |
| language | English |
| last_indexed | 2024-12-12T08:54:04Z |
| publishDate | 2019-12-01 |
| publisher | Kaunas University of Technology |
| record_format | Article |
| series | Elektronika ir Elektrotechnika |
| spelling | doaj.art-8e841acdc4d042c8a3892171567a9d192022-12-22T00:30:04ZengKaunas University of TechnologyElektronika ir Elektrotechnika1392-12152029-57312019-12-01256556110.5755/j01.eie.25.6.2482724827Research on Big Data Mining Technology of Electric Vehicle Charging BehaviourYujun LiuYi HongCheng HuThousands of electric vehicles (EV), which are large in number and flexible in their use of electricity, will be connected to the power system in the near future, which will bring more uncertainty to the power system. Therefore, it is necessary to study the general characteristics of EV charging behaviours. In the charging process, big data regarding charging behaviour of EVs are generated. This paper proposes a big data mining technique based on Random Forest and Principle Component Analysis for EV charging behaviour to identify and analyse clusters with different charging characteristics from the big data. This paper uses Dundee’s January 2018 EV charging data to conduct experiments, and obtains the charging behaviour clusters of the workdays, weekends, and holidays of January. The superiority of the random forest algorithm in the EV clustering problem is reflected when compared to the Euclidean distance method. The clusters obtained by the random forest algorithm have clearer characteristics, including the user’s charging method and travel behaviour. The results show that the charging behaviour of EVs has certain regularity, and the charging load has obvious peak-to-valley difference that is necessary to be regulated.http://eejournal.ktu.lt/index.php/elt/article/view/24827electric vehiclecharging behaviourbig datarandom forestcluster analysis |
| spellingShingle | Yujun Liu Yi Hong Cheng Hu Research on Big Data Mining Technology of Electric Vehicle Charging Behaviour Elektronika ir Elektrotechnika electric vehicle charging behaviour big data random forest cluster analysis |
| title | Research on Big Data Mining Technology of Electric Vehicle Charging Behaviour |
| title_full | Research on Big Data Mining Technology of Electric Vehicle Charging Behaviour |
| title_fullStr | Research on Big Data Mining Technology of Electric Vehicle Charging Behaviour |
| title_full_unstemmed | Research on Big Data Mining Technology of Electric Vehicle Charging Behaviour |
| title_short | Research on Big Data Mining Technology of Electric Vehicle Charging Behaviour |
| title_sort | research on big data mining technology of electric vehicle charging behaviour |
| topic | electric vehicle charging behaviour big data random forest cluster analysis |
| url | http://eejournal.ktu.lt/index.php/elt/article/view/24827 |
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