From high-energy demands to nZEB: the retrofit of a school in Catalonia, Spain
Bibliographic record
Abstract
Since existing buildings are responsible for almost 40% of the energy consumption, a major focus in the construction sector is represented by building energy-saving retrofits. Considering the limited current economic possibilities in the construction market, the retrofit of existing buildings has become a target for both private actors and public administrations in most of European countries. In this context, the province of Catalonia in Spain is defining strategic plans and guidelines to help energy retrofits according to nZEB criteria. In this framework, the present paper evaluates the effectiveness of a series of strategies considered in the energy refurbishment of a school located in the Metropolitan Area of Barcelona. Firstly, the study reports the results of an extensive (level three) energy audit. This data is then used to create a reliable energy simulation model. A set of possible interventions is hence investigated considering technical feasibility, cost, and potential energy saving and indoor comfort benefits. Results show the actual possibility of reaching the nZEB standard at the end of the refurbishment of this high-energy consuming building. Finally, the case study is discussed as a valuable example for promoting energy retrofits in Catalonia and beyond.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".