StateEVMan: Advanced Predictive Ensemble Optimization of Electric Vehicle Charging Stations

• Published in: 2024 9th International Conference on Technology and Energy Management (ICTEM) pp. 1 - 6
• Main Authors: Safari, Ashkan, Gharehbagh, Hamed Kheirandish, Nazari-Heris, Morteza, Kharrati, Hamed, Rahimi, Afshin
• Format: Conference Proceeding
• Language: English
• Published: IEEE 14.02.2024
• Subjects: Accuracy; Costs; Doubly-Fed LSTM; Electric vehicle charging; Electric Vehicle Charging Stations; Forecasting; Key performance indicator; Optimization; Precision Forecasting; Predictive models; Stakeholders; Transportation
• DOI: 10.1109/ICTEM60690.2024.10631928

Optimizing electric vehicle charging stations through advanced predictive ensemble techniques is essential for enhancing efficiency, reducing operational costs, and promoting the widespread adoption of electric vehicles. This approach plays a pivotal role in ensuring seamless charging experiences, thereby advancing the transition to a sustainable and eco-friendly transportation system. By this regard, the proposed paper presents StateEVMan, a novel approach employing doubly-fed Long ShortTerm Memory (LSTM) techniques in conjunction with a comprehensive Electric Vehicle (EV) station dataset. Utilizing stacked ensemble learning, the model predicts three key performance indicators (KPIs): Charging Time [Hour], Total Power Output [kWh], and Total Cost [{\}]. The study assesses the model's performance using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R 2 ) metrics across a dataset comprising 10,185 data points. Notably, the model achieves accurate predictions for these KPIs, demonstrating its robust forecasting capabilities. StateEVMan emerges as a considerable tool for optimizing EV charging station operations and enhancing efficiency.