A Review Of Canadian Typical Year Weather Files For Residential Energy Simulation
Bibliographic record
Abstract
The present study identifies changes in energy simulation of residential buildings in Toronto when three distinct weather simulation methodologies are used. The first is the Typical Year Canadian Weather File for Energy Calculations, the second is the actual meteorological data from the years of 1998 to 2018, and the last is an updated Canadian Weather File for Energy Calculations. The modelled buildings include a single-family home, high rise multi-unit residential building, and low rise multi-unit residential building. Missing meteorological data from the years 2015 to 2018 were collected from Environment Canada Historical Database and National Solar Radiation Database from the National Research Council Laboratory. The results show between a range of 12% monthly variance in energy consumption for low rise buildings, a range of 15% monthly variance in high rise buildings, and a range of 11% variance in single family homes. Annual variances range 2% in total energy variances. The single-family home is verified to an actual home. These results suggest that the monthly values when in a typical year simulation are not indicative of long term actual climate. In addition, this research analyzes the differences in selected months of the Canadian Weather File for Energy Calculations when the historical dataset used to generate the file is changed. Based on the gradual increase in CDD and decrease in HDD, simulations using an updated CWEC represent a climate condition with less heating demand and more cooling demand.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.022 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".