Dispatchable Electric Vehicle Capacity Prediction Model for Power Systems Based on Improved LSTM Algorithm

The integration of electric vehicles (EVs) into power systems is crucial for sustainability but poses prediction challenges for dispatchable EV capacity, essential for grid stability. This paper presents a novel capacity prediction model for power systems using an improved long short-term memory (LSTM) algorithm. Our enhanced LSTM model addresses the limitations of traditional models by optimizing architecture and integrating power system-specific features. Through extensive data analysis and model adjustments, our approach outshines existing methods in accuracy and reliability, showcasing significant improvements in EV capacity forecasting. The simulation results verifies the effectiveness of the proposed method.