Performance of an advanced heat recovery ventilation system in the Canadian Arctic
Bibliographic record
Abstract
A demonstration house was previously built and commissioned in Iqaluit, Nunavut, Canada. The purpose of the overall effort is to develop and integrate technologies and evaluate the performance of a high-performance building located in the Canadian Arctic, while considering the unique social, economic, and logistical challenges associated with its remote location. Previous work consisted of monitoring and reporting on the energy use from heating between April 2016 and April 2017. The purpose of this next stage of research is to contribute experimental data of the prototype demand-controlled residential ventilation system in the extremely cold climate of Iqaluit, where the average annual outdoor temperature is approximately −9 °C. This paper outlines the development, implementation and monitoring of the carbon dioxide-based demand-controlled heat recovery ventilation system that took place between April 2017 and April 2019. The system was equipped with two electric preheaters to ensure that frost build-up did not occur in the heat recovery ventilator (HRV) and adequate ventilation could be maintained according to the demand. An electric heater was included after the HRV to control the supply air temperature. Between December 2018 and February 2019 the electricity consumption of the HRV, preheaters, and supply air heater were measured for the lowest ventilation rate of the system, 15.5 L/s. Pertinent temperatures in the ventilation system were also monitored to enable assessment of the system’s performance. A comparison of the sensible recovery efficiency (SRE) of the HRV and overall system is presented. Experiments displayed that, on average, the SRE of the HRV and system were 72% and 35%, respectively. The total energy use of the ventilation system was 390 kWh over the two months, which translates to 6.30 kWh/day, an energy use intensity of 0.27 kWh/m2/day, or 12.25 Wh/m3 of outdoor air supplied.
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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 | 0.000 |
| 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 | 0.000 | 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".