نهمین کنفرانس بین المللی فناوری و مدیریت انرژی

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کد مقاله icemt9-1058

عنوان StateEVMan: Advanced Predictive Ensemble Optimization of Electric Vehicle Charging Stations

نویسندگان Ashkan Safari - Hamed Kheirandish Gharehbagh - Morteza Nazari-Heris - Hamed Kharrati - Afshin Rahimi

چکیده Optimizing electric vehicle charging stations through advanced predictive ensemble techniques is essential for enhancing efficien-cy, 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 sys-tem. By this regard, the proposed paper presents StateEVMan, a novel approach employing doubly-fed Long Short-Term 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 (R²) 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 consid-erable tool for optimizing EV charging station operations and enhancing efficiency.

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