Accurate forecasting of municipal electric vehicle (EV) charging demand is increasingly important for distribution system planning, charging infrastructure management, and demand-side operation. This study proposes a weather-aware Transformer–LSTM hybrid framework for spatio-temporal forecasting of EV charging load across municipal public charging stations. The proposed approach integrates multi-source information within a unified pipeline, including cyclic temporal encodings, multi-lag autoregressive features, rolling statistics, behavioral aggregates, and meteorological variables, while combining a Transformer encoder to capture long-range temporal dependencies with an LSTM decoder to model local sequential dynamics and nonlinear load patterns. The framework was evaluated using 211,324 charging sessions collected from eight New York City municipal charging stations between July 2021 and December 2025. Under controlled benchmarking against Simple RNN, standalone LSTM, and encoder-only Transformer models using identical preprocessing, feature engineering, and training settings, the proposed hybrid model achieved R² = 0.9731, MAE = 62.71 kWh, RMSE = 94.21 kWh, and MAPE = 19.62%. Relative to the standalone Transformer, the proposed model reduced RMSE by 32.6% and MAPE by 34.5%. In addition, the model maintained strong forecasting performance across stations with heterogeneous demand profiles without station-specific retraining and remained robust across seasonal variations. These results demonstrate that the proposed framework provides a reproducible and scalable solution for municipal EV charging load forecasting in real-world urban environments.
Das et al. (Mon,) studied this question.