Holistic approaches to building retrofit optimization: fusing surrogate modelling with multi-criteria decision methods
Bibliographic record
Abstract
Abstract Building retrofit optimization faces inherent complexity due to competing objectives and high computational demands. This study introduces a methodology leveraging surrogate modelling to enhance computational efficiency while incorporating energy consumption, embodied carbon emissions, and capital costs. The framework integrates NSGA-II multi- objective optimization with three multi-criteria decision-making (MCDM) techniques (AHP, TOPSIS, and VIKOR) across four prioritization scenarios: equal weighting, energy-focused, cost-conscious, and carbon-oriented. Retrofit measures are evaluated across fenestration (WWR and glazing), HVAC systems, and envelope categories. The comparative analysis reveals significant variations between MCDM approaches. The comparative analysis demonstrates that methodological selection significantly influences retrofit optimization outcomes, with observable ranking disparities across the three MCDM techniques. Analysis of the highest- performing 60 configurations across all retrofit categories confirms substantial positional variations along the Pareto front, indicating that each MCDM approach inherently prioritizes different solution characteristics despite evaluating identical performance criteria. The integrated multi-MCDM aggregation framework enhances decision-making reliability by synthesizing these methodological differences into robust retrofit recommendations for diverse stakeholder preferences.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".