Cost Benchmarking of Generation Utilities Using DEA: A Case Study of India
Bibliographic record
Abstract
Technical efficiency of electric utility is the critical element for its competitiveness in the electricity market and very relevant in the Indian electricity sector presently. This paper is aimed to measure the efficiencies of 30 state owned electric generation utilities/companies for the year 2007-08 by applying DEA models with single input and two outputs. The input used is total cost and outputs are units of energy generated and total energy sold or consumed. Cost benchmarking has been carried out so that cost controls can be implemented. In addition, the target evaluation for input cost has also been done. The result of this model shows that GENCOs are generally inefficient in cost frontier and there is an urgent need for intro inspection. This will help for GENCOs. The result shows that the total average of overall, technical and scale efficiencies are 46%, 75.1% and 60% respectively. This efficiency measurement assists the utilities by identifying their shortcomings, setting targets and trying to reach the set targets.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".