Energy usage in buildings for future climate: a case study of Concordia University Buildings in Montreal
Bibliographic record
Abstract
In light of escalating extreme events and climate change, this research focuses on understanding energy consumption in buildings, specifically under varied weather scenarios including past (2019-2022) and future projections (2061, 2099). Traditional building simulations stemming from representative, using typical year’s weather data doesn't capture the intricacies of long-term climate shifts especially for the future. To address this, this study incorporates detailed future climate data from combination of RCMs & GCMs. This data is used in combination with open-geospatial data to create a building geometry. Initial results highlight a shift to warmer temperatures in 2061 and 2099. When contrasted with a typical mean weather scenario (1960-1986), there's a noticeable increase in cooling energy and a decrease in heating energy consumption from 2019-2022. By 2099, overall energy use is predicted to decrease by 10%-30%, which when broken down constitutes to reduction in heating energy and increase in cooling energy. The research underscores the impending shift towards increased cooling demands and reduced heating needs. The findings emphasize the urgency for future building designs to be energy-efficient and resilient in the face of evolving climate conditions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".