Secure and efficient prediction of electric vehicle charging demand usingα^(2)-LSTM and AES-128 cryptography

作     者：Manish Bharat Ritesh Dash K.Jyotheeswara Reddy A.S.R.Murty Dhanamjayulu C. S.M.Muyeen 

作者机构：Department of Electrical and Electronics EngineeringVisvesvaraya Technological UniversityBelgaumIndia School of Electrical and Electronics EngineeringREVA UniversityBangaloreIndia School of Electrical EngineeringVellore Institute of TechnologyVelloreIndia Department of Electrical EngineeringQatar UniversityDoha2713Qatar 

出 版 物：《Energy and AI》 (能源与人工智能（英文）)

年 卷 期：2024年第16卷第2期

页      面：84-100页

核心收录：

学科分类：08[工学] 0807[工学-动力工程及工程热物理] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：The publication of this article was funded Qatar National Library 

主　　题：Charging demand forecasting Deep neural network Electric vehicles LSTM Peak demand management 

摘      要：In recent years,there has been a significant surge in demand for electric vehicles(EVs),necessitating accurate prediction of EV charging *** prediction plays a crucial role in evaluating its impact on the power grid,encompassing power management and peak demand *** this paper,a novel deep neural network based onα^(2)-LSTM is proposed to predict the demand for charging from electric vehicles at a 15-minute time ***,we employ AES-128 for station quantization and secure communication with *** proposed algorithm achieves a 9.2%reduction in both the Root Mean Square Error(RMSE)and the mean absolute error compared to LSTM,along with a 13.01%increase in demand *** present a 12-month prediction of EV charging demand at charging stations,accompanied by an effective comparative analysis of Mean Absolute Percentage Error(MAPE)and Mean Percentage Error(MPE)over the last five years using our proposed *** prediction analysis has been conducted using Python programming.