Simulation of energy impact of an energy recovery ventilator in Northern housing
Bibliographic record
Abstract
The single core Energy Recovery Ventilator (ERV) used in this study is equipped with defrost control that recirculates the exhaust indoor air, while keeps the outdoor air intake damper closed. This defrost strategy has the disadvantage of reducing the outdoor air supplied to the house, which may affect the indoor air quality. First, this paper presents new correlation-based models of supply air temperature T2 after the energy recovery core during normal and defrost operation modes based on laboratory experimental data. A pre-heating coil heats the supply air from T2 to indoor air temperature. Second, a house in Montreal (4356 HDD) is simulated as a reference using TRNSYS program. Since the program cannot simulate the operation under defrost mode, the new models are connected in TRNSYS using equation boxes. The energy use of houses at three locations in northern Canada with HDD of 8798 (Inuvik), 8888 (Kuujjuaq) and 12208 (Resolute), are also simulated, without and with ERV unit. The seasonal energy used for heating the house and pre-heating the supply air is compared with results from Montreal. Compared to the case without heat recovery, the ERV unit leads to energy savings: 24% (Montreal), 26% (Inuvik), 27% (Kuujjuaq), and 27% (Resolute). Compared to the minimum standard requirements, the outdoor airflow rate due to defrost is reduced by 4.7% (223 hours) in Montreal, 19% (1043 hours) in Inuvik, 13% (701 hours) in Kuujjuaq, and 24% (1379 hours) in Resolute.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".