Improving Energy Savings of a Library Building through Mixed Mode Hybrid Ventilation
Bibliographic record
Abstract
In Canada both residential and commercial buildings often require intensive ventilation and air-conditioning to maintain occupant’s thermal comfort and indoor air quality during the operational hours in cooling season. One way to reduce the cooling load consumption is utilizing mixed-mode cooling approach for space conditioning through natural ventilation. This paper presents the potential of control strategies for motorized window opening schedules to reduce the cooling load for a library building that was designed to be net-zero in terms of annual energy consumption (Canada’s first institutional net-zero energy building in Varennes (near Montreal), Québec, Canada). Even though the building is located in cold climate zone, the performance study shows that the building is cooling dominated i.e., it has more cooling load than heating load. To achieve net-zero energy building status, the potential of mixed-mode cooling approach (natural ventilation combined with mechanical ventilation) during the cooling season needs to be investigated. Preliminary simulation results show that the mixed-mode cooling could achieve 10 to 20% energy savings based on hybrid ventilation (HV) with fixed schedules, whereas 65% savings based on HV with variable schedules. This paper also shows 47% reduction of cooling load could be achieved by applying shading control strategies over without shading control strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".