Evaluation of Efficiency and Savings: Some Remarks on the Influence of Energetic and Financial Parameters
Bibliographic record
Abstract
Assessing energy savings is a complex task due to the lack of direct measurement tools.Meters record consumption, but there is no direct way to measure savings.Instead, savings are estimated by comparing the projected consumption without efficiency improvements to the actual consumption after the interventions.This is not a strict before-and-after comparison but rather an evaluation that accounts for variables such as climate conditions, operational factors, and other influences on energy consumption.This paper is based on real-world applications, carried out within the energy diagnosis process and supported by the implementation of the IPMVP® protocol.The study focuses on the economic analysis of some investments, highlighting the importance of determining their profitability, which can influence their attractiveness to an Energy Service Company (ESCO).The results of sensitivity analyses are reported, focusing on evaluations based on assumptions that are particularly important, especially in times of high uncertainty regarding energy prices, interest rates, and energy production scenarios.The paper reports on these analyses, identifying key parameters to focus on and establishing suitable criteria for decision-making.
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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 | 0.012 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".