Cost optimal analysis of energy refurbishment actions depending on the local climate and its variations
Bibliographic record
Abstract
The cost optimal methodology indicated by the European Directive 2010/31/UE represents the starting point to support decisions for refurbishment interventions.The procedure is much more useful if considering the limited available investments from Public Authorities or privates and represents an important decision tool to define the owners' constraints and the economic commitment in the years.However, the results are usually affected by the influence of some variables, among which there are the climatic conditions, subject of the present investigation.To quantify some effects, the best costs/benefits ratio is evaluated among some improvement scenarios built on a series of energy efficiency measures, in the climatic conditions of some locations.The first set of calculations compares energy performance and global costs referring to climatic data taken from the Italian National Standard UNI 10349 edition 1994 and 2016.The second analysis is performed, for one location, on the basis of the two reference climatic datasets and the registered climatic data of the last 20 years.From the results analysis, it can be observed the need of indications by the European Commission on a regularly reference climatic data updating, to guarantee in all the countries affordable calculations for the cost optimal refurbishment solutions, and on the introduction of a tolerance/confidence range to take into account the real climate variations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".