Unsupervised Nonintrusive Extraction of Electrical Vehicle Charging Load Patterns
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Extracting electric vehicle (EV) charging loads is an important aspect that enables smart grid operators to make informed and intelligent decisions about conserving power and promoting the reliability of the electrical grid. This paper presents an unsupervised algorithm to extract the EV charging loads (EVCLs) nonintrusively from the smart meter data. The proposed algorithm can run on low-frequency smart meter sampling data and only requires the real power measurement, which is the type of data communicated and recorded by most smart meters. Validation results on real aggregated household loads have shown that the proposed approach is a promising solution to extract EVCLs and that the approach can effectively mitigate the interference of other appliances that have similar load behaviors as EVs. Furthermore, the extraction of such load behaviors can be aggregated and open further smart grid analyses and studies.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it