Informing building retrofits at low computational costs: a multi-objective optimisation using machine learning surrogates of building performance simulation models
Bibliographic record
Abstract
Machine learning (ML) algorithms are increasingly used as surrogates for building performance simulation (BPS) models to leverage their energy predictive capabilities while reducing computational costs. In parallel, researchers are developing optimisation methods to inform building design and retrofit strategies but rarely employ ML-based BPS surrogates for this purpose. This study proposes a coupled modelling approach that leverages the capabilities of ML-based BPS surrogate models and multi-objective optimisation to inform holistic design and operation retrofits at low computational costs. The proposed methodology is demonstrated using an archetypal office building in Ottawa, Canada. The developed models achieved competitive predictive accuracies (adjusted R2: 0.90–0.99), identifying total and peak energy saving measures with up to 34% improvement in occupant thermal comfort at computational speeds 1266 times faster than a traditional BPS-based optimisation approach. Results offer a promising modelling workflow for design applications requiring extensive computations and scenario analyses, such as net-zero energy retrofits.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".