Feasibility of using diesel generation according to energy consumption and demand in a meat-packing company
Bibliographic record
Abstract
In order to maintain the security of the electricity supply, a meat-packing company located in western Paraná has seven diesel generators in its generator set, which are kept in conditions for immediate operation in the event of a power outage from the concessionaire. This study seeks to verify the economic viability for this generator set to perform peak demand cuts, or to be used during peak hours, reducing electricity expenses. To this end, it was necessary to acquire the mass memorial with consumption and demand data for a period of one year, energy bills, generation data and expenses of the diesel generator set. In this study, three possible cases were verified. In addition to the rates practiced by the free market to which this meat-packing company is served, cases were verified in which the meat-packing company was in the captive market where it would be possible to be classified in the Blue tariff mode and Green tariff mode of group A3a with Copel. The annual expenditure on electricity (consumption + demand) in the Free Market is approximately R$11.9 million, for the Blue tariff modality it would be approximately R$14.7 million, and for the Green tariff modality it would be approximately R$14.3 million. It was found that the demand values have little variation throughout the day, and little variation throughout the year, and therefore the generators were not used to cut peak demand. The only viable case for using diesel generators would be during peak hours for the Green tariff modality. The results indicate that participation in the Free Market brings savings in electricity of at least 20% compared to the captive market.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".