Approximate Methods for Event-Based Customer Interruption Cost Evaluation
Bibliographic record
Abstract
There is increasing interest in the new deregulated electric utility environment in assessing the customer costs associated with failures in electric power supply and the responsibilities associated with these failures. The customer interruption cost when an electric supply failure occurs depends on many factors, such as the customer types interrupted, the actual load demand at the time of the outage, the duration of the outage, the time of day and the day in which the outage occurs. The absence of many of the data sets required in a detailed evaluation of the customer costs makes it difficult to estimate precise individual customer outage costs due to a specific failure event. This paper illustrates the development of approximate methods for event-based customer interruption cost evaluation on a distribution feeder subjected to a specific outage event. A series of approximate methods are presented and the outage cost estimates are compared with a set of base method results. The approximate methods are based on the use of customer sector data sets, i.e., commercial, industrial, residential data, which are generally available to most utilities. The paper also illustrates further simplifications of the approximate techniques which reduce the effort required to estimate the outage costs. The approximate techniques provide a practical approach to evaluating a specific outage event.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".