Adapting building performance simulation for climate resilience: accounting for urban microclimates and future climates
Bibliographic record
Abstract
Climate change intensifies extreme events, increasing risks to comfort, thermal stress, and occupant health. To respond, buildings must be designed and operated for climate resilience, which heavily depends on advancing building performance simulation (BPS) tools and practices. Traditional BPS primarily focuses on annual or seasonal performance using historical ‘typical’ or projected weather data, often limited to a single building or a single projection future scenario, and overlooking key factors affecting urban performance. This viewpoint paper critically examines how BPS needs to evolve to support climate resilience. We first identify the limitations of conventional BPS and emphasize the need to scale from individual buildings to neighbourhoods and urban districts, addressing a wide range of climates and extreme conditions. Next, we highlight the importance of advanced downscaling techniques, multi-year climate projections, advanced metrics, and microclimate analysis. In summary, achieving climate-resilient BPS requires broadening both spatial and temporal scales for future-ready building design.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".