Retrofitting for the future: Analysing the sensitivity of various retrofits to future climate scenarios while maintaining thermal comfort
Bibliographic record
Abstract
Significant research has explored the impact of future climate scenarios on building energy loads; however, few studies have considered the efficiency of envelop retrofits under a changing climate and most have a very limited geographic scope. Using a range of future climate scenarios based on global average temperature rise, rather than specific models, this paper presents a multi-region, multi-scenario analysis of retrofits for large office buildings for two ASHRAE climate zones. Three levels of envelope intervention were developed based on currently available materials and current practices to explore the individual and combined impacts of increased roof and wall insulation, and glazing replacement. The simulations used the ASHRAE 90.1–2019 prototypical building model equipped with a variable air volume (VAV) HVAC system served by electric chillers with a reference COP of 6.28 and natural gas boilers with a nominal thermal efficiency of 81.25%. These were simulated in each climate scenario and total heating and cooling energy were compared with the unmodified ASHRAE 90.1–2019 prototypical building model. Thermal comfort was also considered through unmet hours to ensure like-for-like comparison. All retrofits tested demonstrated high sensitivity to climate change, with energy use increasing by up to 18% for cooling and decreasing by up to 41% for heating. By analysing the outputs using regression models, a series of equations were developed to predict retrofit impacts as a function of future climate change. Increased insulation—in roof and wall—beyond the baseline was found to have limited benefit while glazing replacement offered significant value.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".