The Application of Improved Random Forest Algorithm on the Prediction of Electric Vehicle Charging Load

Yiqi Lu, Yongpan Li, Da Xie, Enwei Wei, Xianlu Bao, Huafeng Chen and Xiancheng Zhong
Additional contact information
Yiqi Lu: Shanghai Jiao Tong University, Minhang District, Shanghai 200240, China
Yongpan Li: Shenzhen Power Supply Co. Ltd., Luohu District, Shenzhen 518001, China
Da Xie: Shanghai Jiao Tong University, Minhang District, Shanghai 200240, China
Enwei Wei: Shenzhen Comtop Information Technology Co. Ltd., Shenzhen 518034, China
Xianlu Bao: Shenzhen Comtop Information Technology Co. Ltd., Shenzhen 518034, China
Huafeng Chen: Shenzhen Power Supply Co. Ltd., Luohu District, Shenzhen 518001, China
Xiancheng Zhong: Shenzhen Comtop Information Technology Co. Ltd., Shenzhen 518034, China

Energies, 2018, vol. 11, issue 11, 1-16

Abstract: To cope with the increasing charging demand of electric vehicle (EV), this paper presents a forecasting method of EV charging load based on random forest algorithm (RF) and the load data of a single charging station. This method is completed by the classification and regression tree (CART) algorithm to realize short-term forecast for the station. At the same time, the prediction algorithm of the daily charging capacity of charging stations with different scales and locations is proposed. By combining the regression and classification algorithms, the effective learning of a large amount of historical charging data is completed. The characteristic data is divided from different aspects, realizing the establishment of RF and the effective prediction of fluctuate charging load. By analyzing the data of each charging station in Shenzhen from the aspect of time and space, the algorithm is put into practice. The application form of current data in the algorithm is determined, and the accuracy of the prediction algorithm is verified to be reliable and practical. It can provide a reference for both power suppliers and users through the prediction of charging load.

Keywords: electric vehicle (EV); random forest; charging load; data analysis; load forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

Downloads: (external link)
https://www.mdpi.com/1996-1073/11/11/3207/pdf (application/pdf)
https://www.mdpi.com/1996-1073/11/11/3207/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:11:y:2018:i:11:p:3207-:d:183837

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().