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

Yiqi Lu,Huafeng Chen,Xiancheng Zhong,Xianlu Bao,Da Xie,Enwei Wei,Yongpan Li

doi:10.3390/en11113207

Yiqi Lu, Huafeng Chen + Show 5 more

Open Access

https://doi.org/10.3390/en11113207

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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.

Highlights

To solve the problems such as load balancing, capacity planning, and power quality caused by the access of large-scale electric vehicle (EV) [1], researchers have proposed many practical coordinated control schemes to guarantee the safety and reliability of the power system
The mean absolute percentage error (MAPE) and the root mean square error are used for evaluation
This paper proposes a method based on random forest algorithm (RF) for EV charging load prediction and analysis, and apply it on Shenzhen actual charging data and application scenarios, and draws the following conclusions: (1)

Summary

Introduction

To solve the problems such as load balancing, capacity planning, and power quality caused by the access of large-scale electric vehicle (EV) [1], researchers have proposed many practical coordinated control schemes to guarantee the safety and reliability of the power system. After the analyzing of the load demand in certain area, the EV chargers can be used to balance the unbalanced network without overloading the charger [2]. It has been proved that the load of EV can be converted into a tool to benefit the power system [3] by applying optimal charging schemes to arrange the charging and discharging through certain approaches such as demand side response [4,5]. These methods depend on the load prediction to a certain extent. Models can be established to simulate the fluctuation of EV load [7], and the load can be forecasted and it can be used as a feasible load to reduce the pressure of Energies 2018, 11, 3207; doi:10.3390/en11113207 www.mdpi.com/journal/energies

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