Descriptor: Simon Fraser University Electric Vehicle Parking Dataset (SFU-EVP)
Bibliographic record
Abstract
Simon Fraser University (SFU) aims to make a significant contribution to the study of electric vehicle (EV) utilization and power grid management by providing a comprehensive dataset [Simon Fraser University electric vehicle parking dataset (SFU-EVP)] of EV charging sessions since 2019. This dataset will be continually updated in the future. This extensive dataset presents valuable information on EV charging patterns, providing critical input for power grid planning, policy development, rate design, and infrastructure placement. It also offers opportunities to improve load forecasting, ensure grid stability, and improve the integration of renewable energy. Furthermore, data can facilitate research toward optimizing various vehicle-to-grid (V2G) services, including harnessing EVs as distributed energy storage systems. All data are stored in the commonly used and easily accessible comma-separated value (CSV) file format. By making this dataset publicly available, SFU has created a vital dataset that can drive further innovation and efficiency in EV technology and grid management, fostering a more sustainable and environmentally friendly future.IEEE SOCIETY/COUNCILPower and Energy Society (PES)DATA TYPE/LOCATIONTime-Series; SFU Campuses, Metro Vancouver, CanadaDATA DOI/PID10.21227/ya1w-m583
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.042 | 0.072 |
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".