Implementation of urban building energy modeling in historic districts. Seville as case- study
Bibliographic record
Abstract
Buildings represent 40% of the European Union's final energy consumption and are largely of residential use.From 2006 to 2016, existing European housing stocks have been analysed at national level to make the energy refurbishment processes transparent and effective.However, at the metascale of regions, cities or neighbourhoods, case-by-case analysis using Building Energy Models (BEM) becomes an unfeasible decision-support tool.To try to overcome this limitation, the nascent field of Urban Building Energy Modelling (UBEM) is making substantial progress in the assessment of building energy performance at urban scale.Still, most of the UBEM projects rely upon archetypes -i.e.virtual or sample buildings illustrative of the most frequent characteristics of a particular category, and the definition and description of such archetypes may compromise their reliability.This paper presents an alternative UBEM approach, especially designed for the homogeneous historic districts of cities where a significant proportion of the buildings are under preservation rules.These rules can restrict the scope of the measures to improve their energy efficiency or limit the possibility of implementing renewable energy systems.We introduce a new parameter (HAD) to classify blocks according to their heritage asset density.HAD is then mapped onto the study-area and the sample block is selected as representative of the most frequent HAD category.Using the historic ensemble of Seville as case-study, this paper shows results in energy consumption on a district scale and proposes a set of solutions to improve the energy efficiency of the buildings while respecting the heritage preservation rules.To support consistent policy decisions, validation of these results has been carried out, by in-situ monitoring of a representative number of dwellings.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".