StateEVMan: Advanced Predictive Ensemble Optimization of Electric Vehicle Charging Stations
Bibliographic record
Abstract
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 (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 considerable tool for optimizing EV charging station operations and enhancing efficiency.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".