Characterization of Space Conditioning Loads for Energy Efficient Houses in Canada
Bibliographic record
Abstract
This thesis details the development of a graphical method of presenting equipment loads within a house, allowing the loads for an entire year to be presented on a psychrometric chart. These charts are called rosettes. This graphical method allows the magnitude and distribution of both the sensible and latent loads to be examined, and for changes throughout the year to be examined. The rosettes can be used to determine whether or not conventional HVAC equipment is the best equipment for maintaining occupant comfort within the house, or if different equipment must be considered. This is of particular importance in highly energy efficient houses. \n \nThe rosettes also allow the split between the sensible and latent loads to be examined. In conventional houses, the loads are dominated by sensible loads, especially sensible heating. However, it is already known that energy efficient houses require supplemental ventilation to avoid the development of moisture problems within the house. It was expected that in a highly insulated house with both heat and moisture control equipment, the loads will be dominated instead by latent loads instead of sensible. In such houses, it was expected that supplemental moisture control equipment may be required. The rosettes were used to determine if this was indeed the case. \n \nSimulations of six different houses were run using the program ESP-r, and the results \nwere used to create rosettes for each simulation. The simulations examined different constructions, an increased number of occupants, the addition of active shading control, and the use of an alternative control scheme. The sensible and latent loads for each case are examined and discussed. \n \nRosettes produced from this study showed that improving the standard of construction \nhas a significant effect on the equipment loads. However, the rosettes did not show a \ndramatic shift from sensibly dominated loads to latent dominated loads. The results show that changing the occupancy has the greatest effect on the equipment loads of the houses simulated in this study. The rosettes also show that while the shift from sensible loads to latent loads is not as dramatic as expected, there was a significant increase in the sensible cooling requirements.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".