System reliability worth assessment using the customer survey approach
Bibliographic record
Abstract
A very popular method utilized in quantifying the benefit of power delivery service reliability is to estimate the customer monetary losses associated with power supply interruptions by collecting data with customer surveys. MidAmerican Energy Company, a Midwest utility, recently performed surveys of its customers in the residential, commercial, industrial and company/organization sectors. This work presents the industrial and commercial customer results of these surveys with primary focus on the cost results. The results of this study are compared with the results of other studies performed in high cost areas of the United States east and west coasts. This is the First ever study of this nature performed for the electrical customers in the United States Midwest region. Methodological differences in study design compared to coastal surveys are discussed. The major contribution of this paper is that particulars of Midwest customers compared to customers of coastal utilities are noted, the impact on customers with backup supply is identified, a suggested approach to enhancing customer satisfaction due to advance warning on outages is recommended, and relatively high survey response return rates are elaborated. The customer damage functions derived from the survey results are being routinely used in power delivery project justifications in annual delivery system budgeting process.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".