Goodness of Fit Statistical Analysis for Different Variables of PEV Driver Behaviour
Bibliographic record
Abstract
Modelling plug-in electric vehicles (PEVs) charging load for use in many power system applications requires reliable estimates of a number of random variables that characterize the PEV charging process. Among these variables are the variables relevant to the driver's behaviour (e.g., arrival and departure times and daily mileage). Determining reliable estimates of these variables is challenging, since no currently sufficient real data can be relied upon for precise descriptions of these variables. The alternative is to use sample data for each variable from the available transportation mobility data, and to estimate a proper probability distribution function (PDF) that can preserve the random characteristics of each variable and generate the desired synthetic data. This paper presents a statistical evaluation study for different collections of PDFs in order to find the best model to precisely reflect the random characteristics of each driver behaviour variable. The most commonly used PDFs, along with some advanced PDFs, have been verified against the observed sample data based on consideration of a well-known goodness of fit statistical test.
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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 | 0.000 |
| 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) | 0.001 | 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 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".