Accurate forecasting of energy demand for electric vehicles (EVs) is crucial for maintaining the stability and reliability of power systems. By predicting demand over various periods, charging station owners can ensure a continuous energy supply. Medium-term and long-term demand predictions, which extend from a few weeks to several days, help analyze charging demand across different periods based on historical trends. This study proposes a Transformer model that utilizes an LSTM-based encoder-decoder for forecasting the demand at electric vehicle charging stations (EVCS). The proposed model is compared with traditional deep learning-based LSTM and Transformer models. The research employs open datasets from ACN, including charging data from Caltech and JPL. Both datasets are used to train and test the models. Predictions are made for 30 days, 120 days, and 240 days ahead, with results compared to actual demand. Performance is evaluated using Mean Absolute Error (MAE) and Mean Squared Error (MSE). Compared to baseline models, the proposed LSTM-Transformer model for Caltech data shows a significant improvement at the 30-day horizon, lowering MAE by up to 17.27% and MSE by 19.79%. The accuracy improvement are minor but consistent in longer horizons (120 and 240 days), with an MAE and MSE improvement of up to 5.71% and 4.85%, respectively. The LSTM-Transformer model also shows better accuracy across all horizons for JPL data by reducing MAE and MSE by up to 24.91% and 23.17% at 30 days, 5.00% and 5.17% at 120 days, and 3.90% and 4.86% at 240 days. The results indicate that the Hybrid Transformer model outperforms the baseline models for both datasets in medium-term and long-term predictions. The 30, 120, and 240-day predictions demonstrate lower error rates with the proposed model when utilizing Caltech and JPL charging data for these time frames.

The emerging wireless energy transfer technology enables sensor nodes to maintain perpetual operation. However, maximizing the network performance while preserving short charging delay is a great challenge. In this work, a Wireless Mobile Charger (MC) and a directional charger (DC) were deployed to transmit wireless energy to the sensor node to improve the network’s throughput. To the best of our knowledge, this is the first work to optimize the data sensing rate and charging delay by the joint scheduling of an MC and a DC. We proved we could transmit maximum energy to each sensor node to obtain our optimization objective. In our proposed work, a DC selected a total horizon of 360° and then selected the horizon of each specific ${90}^{\circ }$ area based on its antenna orientation. The DC’s orientation was scheduled for each time slot. Furthermore, multiple MCs were used to transmit energy for sensor nodes that could not be covered by the DC. We divided the rechargeable wireless sensor network into several zones via a Voronoi diagram. We deployed a static DC and one MC charging location in each zone to provide wireless charging service jointly. We obtained the optimal charging locations of the MCs in each zone by solving Mix Integral Programming for energy transmission. The optimization objective of our proposed research was to sense maximum data from each sensor node with the help of maximum energy. The lifetime of each sensor network could increase, and the end delay could be maximized, with joint energy transmission. Extensive simulation results demonstrated that our RWSNs were designed to significantly improve network lifetime over the baseline method.

The emerging wireless energy transfer technology enables sensor nodes to maintain perpetual operation. However, maximizing the network performance while preserving short charging delay is a great challenge. In this work, a Wireless Mobile Charger (WMC) and a Directional Charger (DC) are deployed to transmit wireless energy to the sensor node to improve the network's throughput. To the best of our knowledge, this is the first work to optimize the data sensing rate and charging delay by joint scheduling of MC and DC. We prove we can transmit maximum energy to each sensor node to obtain our optimization objective. In our proposed work, a DC selects a total horizon of equipped with DCs, which selects the horizon of each specific area with based on its antenna orientation. DC's orientation is scheduled for each time slot. Furthermore, multiple MCs are used to transmit energy for sensor nodes which cannot be covered by DCs. We divided the Rechargeable Wireless Sensor Network into several zones via the Voronoi diagram. We deployed a static DC and one MC charging location in each zone to provide wireless charging service jointly. We obtain the optimal charging location of MCs in each zone by solving Mix Integral Programming for energy transmission. The optimization objective of our proposed research is to sense maximum data from each sensor node with the help of maximum energy. The lifetime of each sensor network can increase, and the end delay can be maximized with joint energy transmission. Extensive simulation results demonstrate that our RWSNs are designed significantly to improve network lifetime over the baseline method.