Smart City Gnosys

Smart city article details

Title	An In-Depth Analysis Of Electric Vehicle Charging Station Based On Lstm And Svm Hybrid Model
ID_Doc	8309
Authors	Ramkumar V.; Nadaf A.B.; Ghamande M.V.; Dabral A.P.; Bharambe P.M.; Kumar D.
Year	2023
Published	7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023 - Proceedings
DOI	http://dx.doi.org/10.1109/ICECA58529.2023.10395692
Abstract	As part of their future smart city ambitions, many countries hope to increase environmental sustainability by electrifying their transportation networks. This means that the percentage of electric vehicles in any particular city's total vehicle population will rise rapidly. Electric vehicle (EV) batteries can be recharged in a number of ways, but charging stations will be given top priority. Charging stations should be dispersed across a city in such a way that all EVs may reach one within a reasonable period of time and then use that charge to travel the entire city. This data preprocessing phase makes use of normalized processing, time interval processing, and outlier processing. The features are derived using principal component analysis. The LSTM method entails a set of four gates (an input, a forget, a learn, and a remember), All of which contribute to the model's training. The models are tested using an LSTM-SVM method. When compared to LSTM and SVM, the proposed method produces higher-quality results. © 2023 IEEE.
Author Keywords	Long Short-Term Memory (LSTM); Principal component analysis (PCA); Support vector machine (SVM)

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