Secure and efficient prediction of electric vehicle charging demand using α2-LSTM and AES-128 cryptography

In recent years, there has been a significant surge in demand for electric vehicles (EVs), necessitating accurate prediction of EV charging requirements. This prediction plays a crucial role in evaluating its impact on the power grid, encompassing power management and peak demand management. In this paper, a novel deep neural network based on α2 -LSTM is proposed to predict the demand for charging from electric vehicles at a 15-minute time resolution. Additionally, we employ AES-128 for station quantization and secure communication with users. Our 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 accuracy. We 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 model. The prediction analysis has been conducted using Python programming. [Display omitted] •A novel deep neural network based on α2-LSTM for predicting EV charging demand at a 15-minute time resolution is proposed.•The AES-128 for quantizing the station and ensuring secure communication with the user is presented.•Proposed algorithm achieves a 9.2% reduction in both the Root Mean Square Error (RMSE) and the mean absolute error.•The primary objective of this paper is to demonstrate that a large window size of data can be used without compromising efficiency.