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Title	Forecasting The Behavior Of Ev Charging Using Machine Learning
ID_Doc	26861
Authors	Salam S.; Veni G.K.; Kamale C.; Deepika A.L.; Bruhathi A.
Year	2023
Published	2023 9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023
DOI	http://dx.doi.org/10.1109/ICACCS57279.2023.10112731
Abstract	Electric vehicles have become more common nowadays because of their smart mobility in applications for smart cities because it reduces the greenhouse emission. Forecasting the behavior of EV charging where we can predict the level of energy consumption. With the help of attributes such as EV charging station, Location, Session time, Weather, Traffic, Speed and it can predict how much energy is consumed. To estimate the behavior of EV charging we make use of supervised machine learning techniques like SVM, RF, XGBoost and CatBoost. By equating all algorithms, by that we can measure performance of the algorithms. From this, CatBoost shows the higher accuracy when we compared to the other algorithms i.e., the accuracy rate for the Random Forest is 87.55%, SVM is 89.33%, XGBoost is 92.89% and CatBoost is 98.37%. © 2023 IEEE.
Author Keywords	CatBoost; Classification; Random Forest; Supervised Machine Learning Techniques; SVM; XGBoost

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