Target-oriented Benchmarking of Regional Building Energy Consumption Based on the Lorenz Curve
Bibliographic record
Abstract
For the purpose of building energy efficiency, the regional building energy benchmarking is indispensable for a district at planning stage. In this paper, a new approach is proposed by the combination of quota-level analysis and Lorenz curve, instead of the traditional simulation or coarse estimation by indicators. Firstly, the cumulative probability method was conducted to achieve a quota-level table, based on the data from State Grid and field surveys of 38 hotel buildings in the city of Hangzhou. At the same time, the Lorenz curve was introduced to de-scribe the distributed inequality of regional energy consumption. Finally, together with other planning parameters, the regional energy benchmarking was accomplished by accumulation between the given upper and lower EUI limitations along with the Lorenz curve. It should be noted that the pre-set EUI restrictions based on the quota-level analysis can directly represent the requested targets of building energy efficiency at planning stage.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".