Factors Impacting Short-Term Load Forecasting of Charging Station to Electric Vehicle

被引:4
|
作者
Elahe, Md Fazla [1 ,2 ]
Kabir, Md Alamgir [2 ,3 ]
Mahmud, S. M. Hasan [2 ,4 ]
Azim, Riasat [5 ]
机构
[1] Daffodil Int Univ, Dept Software Engn, Dhaka 1216, Bangladesh
[2] Ctr Adv Machine Learning & Applicat CAMLAs, Dhaka 1229, Bangladesh
[3] Malardalen Univ, Sch Innovat Design & Engn, Artificial Intelligence & Intelligent Syst Res Grp, Hogskoleplan 1, S-72220 Vasteras, Sweden
[4] Amer Int Univ Bangladesh AIUB, Dept Comp Sci, Dhaka 1229, Bangladesh
[5] Hunan Univ, Coll Comp Sci & Elect Engn, Changsha 410082, Peoples R China
关键词
charging station; electric vehicle; forecast; power demand; privacy;
D O I
10.3390/electronics12010055
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The rapid growth of electric vehicles (EVs) is likely to endanger the current power system. Forecasting the demand for charging stations is one of the critical issues while mitigating challenges caused by the increased penetration of EVs. Uncovering load-affecting features of the charging station can be beneficial for improving forecasting accuracy. Existing studies mostly forecast electricity demand of charging stations based on load profiling. It is difficult for public EV charging stations to obtain features for load profiling. This paper examines the power demand of two workplace charging stations to address the above-mentioned issue. Eight different types of load-affecting features are discussed in this study without compromising user privacy. We found that the workplace EV charging station exhibits opposite characteristics to the public EV charging station for some factors. Later, the features are used to design the forecasting model. The average accuracy improvement with these features is 42.73% in terms of RMSE. Moreover, the experiments found that summer days are more predictable than winter days. Finally, a state-of-the-art interpretable machine learning technique has been used to identify top contributing features. As the study is conducted on a publicly available dataset and analyzes the root cause of demand change, it can be used as baseline for future research.
引用
收藏
页数:18
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