Parametric analysis of energy recovery ventilation performance in the high rise residential sector: a Toronto case study
Bibliographic record
Abstract
The performance of energy recovery ventilation (ERV) units is analyzed for high rise residential buildings in the Toronto climate. ERV units are scrutinized by their ability to recover heating and cooling energy throughout the year. The rated effectiveness of ERV units is well documented through applicable Standards. Several factors are identified which have the potential to influence the impact of ERVs under actual operation conditions, including infiltration rate, ERV leakage flows, temperature set points, and operation schemes. EnergyPlus is used to represent a typical residential suite, through which the impacts of the parameters are studied. Performance of the ERV model is validated through comparison to collected temperature and humidity data from an operating ERV unit in the GTA. Results indicate that leakage flows within the ERV represent the highest potential contributor to ERV performance. Economizer control strategies are determined as a viable option for improving performance during warmer seasons. The performance of energy recovery ventilation (ERV) units is analyzed for high rise residential buildings in the Toronto climate. ERV units are scrutinized by their ability to recover heating and cooling energy throughout the year. The rated effectiveness of ERV units is well documented through applicable Standards. Several factors are identified which have the potential to influence the impact of ERVs under actual operation conditions, including infiltration rate, ERV leakage flows, temperature set points, and operation schemes. EnergyPlus is used to represent a typical residential suite, through which the impacts of the parameters are studied. Performance of the ERV model is validated through comparison to collected temperature and humidity data from an operating ERV unit in the GTA. Results indicate that leakage flows within the ERV represent the highest potential contributor to ERV performance. Economizer control strategies are determined as a viable option for improving performance during warmer seasons.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".