Modelling of the Net Zero Energy Town House in Toronto Using TRNSYS, and an Analysis of the Impact Using Thermal Mass
Bibliographic record
Abstract
A detailed model of the Net Zero Energy Town House in Toronto is developed in TRNSYS, incorporating a ground source heat pump integrated with an in floor radiant heating system. In order to minimize the heating and cooling loads, the building envelope is well insulated with the exterior walls having an R-60 insulation value. Much of the work done previously on the use of thermal mass in buildings has been experimental in nature and has focussed mainly on conventional brick construction in hot climates such as Asia and Africa. This research will analyze the impact of using thermal mass with a building envelope that is highly insulated, and of a light construction, such as that used in Low Energy or Net Zero housing. Furthermore, this analysis would also evaluate the impact of using thermal mass in a cold climate such as that found in Canada. The simulations showed that, for colder climates, thermal mass can replace some of the insulation and still provide superior results. Also the impact of thermal mass was found to be more significant during the winter season than summer for Toronto.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.007 | 0.001 |
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".