Characterization of Representative Residential Buildings within a Neighborhood and Their Energy Efficiency Levels According to RTQ-R
Bibliographic record
Abstract
Approximately 54% of Brazilian electricity consumption is attributed to the residential, commercial, public, and service sectors; thus, it is important to formulate strategies that promote both the energy efficiency of buildings and a better understanding of their thermal and energy performance. Within the scope of the Brazilian Labeling Program (Programa Brasileiro de Etiquetagem—PBE), technical regulations were developed to classify the level of energy efficiency of buildings. This article defines four representative buildings based on an analysis of the most common typologies that represent the multi-family residential buildings of a neighborhood. A total of 663 buildings were mapped and classified. The four representative buildings were evaluated for their thermal and energy performance in relation to Building Labeling (Quality Technical Regulation–Residential: RTQ-R). The results for the housing units (HUs) were analyzed for cooling degree-hours (cooling for summer), relative consumption for refrigeration (artificial cooling), performance of the envelope in summer, and the final classification of the HUs by the water heating system; the results for the entire multi-family building were analyzed. These results provide data that will contribute to an efficient policy for the housing industry and to future studies on the incorporation of measures that promote energy efficiency.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".