A Novel Attention-Augmented LSTM (AA-LSTM) Model for Optimized Energy Management in EV Charging Stations

Electric Vehicles (EVs) have emerged as a cleaner, low-carbon, and environmentally friendly alternative to traditional internal combustion engine (ICE) vehicles. With the increasing adoption of EVs, they are expected to eventually replace ICE vehicles entirely. However, the rapid growth of EVs has significantly increased energy demand, posing challenges for power grids and infrastructure. This surge in energy demand has driven advancements in developing efficient charging infrastructure and energy management solutions to mitigate the risks of power outages and disruptions caused by the rising number of EVs on the road. To address these challenges, various deep learning (DL) models, such as Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks, have been employed for predicting energy demand at EV charging stations (EVCS). However, these models face certain limitations. They often lack interpretability, treating all input steps equally without assigning greater importance to critical patterns that are more relevant for prediction. Additionally, these models process data sequentially, which makes them computationally slower and less efficient when dealing with large datasets. In the context of these limitations, this paper introduces a novel Attention-Augmented Long Short-Term Memory (AA-LSTM) model. The proposed model integrates an attention mechanism to focus on the most relevant time steps, thereby enhancing its ability to capture long-term dependencies and improve prediction accuracy. By combining the strengths of LSTM networks in handling sequential data with the interpretability and efficiency of the attention mechanism, the AA-LSTM model delivers superior performance. The attention mechanism selectively prioritizes critical parts of the input sequence, reducing the computational burden and making the model faster and more effective. The AA-LSTM model achieves impressive results, demonstrating a Mean Absolute Percentage Error (MAPE) of 3.90% and a Mean Squared Error (MSE) of 0.40, highlighting its accuracy and reliability. These results suggest that the AA-LSTM model is a highly promising solution for predicting energy demand at EVCS, offering improved performance and efficiency compared to contemporary approaches.