EV Charging Profiles and Waveforms Dataset (EV-CPW) and Associated Power Quality Analysis
Bibliographic record
Abstract
The rapid growth of electric vehicle (EV) charging will present challenges to electrical distribution networks and will affect grid operation and reliability. In order to improve the understanding of EV charging behaviour, we present the open-access <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EV Charging Profiles and Waveforms</i> (EV-CPW) dataset for AC charging. The dataset comprises of charging profiles and high-resolution current/voltage AC waveforms for 12 different EV’s, including popular battery EV’s and plug-in hybrid EV’s. A power quality analysis is carried out to compare the EV charging behaviours to new standards recommendations proposed by standards agencies. This includes evaluating power factor, current and voltage distortion, harmonic content and load behaviour in relation to grid voltage and frequency. The preliminary data analysis presented reveals that each EV has distinctive charging characteristics and the power quality analysis indicates variation in the on-board charger circuits employed by the EV’s. The EV-CPW dataset can be used for many more applications and studies, including EV charging infrastructure planning, demand management, EV charging coupled with renewable energy studies, power quality analysis, equipment lifetime studies and power electronics design. The dataset can be accessed at https://IEEE (will be added post review).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".