Bibliographic record
Abstract
Development of net-zero energy house (NZEH) optimization has resulted in NZEHs with much lower building costs.However, a viable pathway to cost-effective NZEHs has not yet been made evident.This study uses the Non-domination Sorting Genetic Algorithm II with active Pareto-archiving (aNSGA-II) and Iterated Sequential Search (ISS) algorithms connected with EnergyPlus to perform energy and cost optimizations on seventeen scenarios to determine how NZEHs can become more cost-effective and what is necessary to make them as or more cost-effective than code-built houses.Three house types are optimized in Ottawa, Ontario: a single detached house, an end unit townhouse and a middle unit townhouse.This research finds that a single detached NZEH can be cost-effective if occupant engagement is pursued, if the price of solar photovoltaics decreases to $1.434/W installed, and if the construction costs are similar to that of a large-scale developer.Most importantly I thank my supervisors, William (Liam) O'Brien, Craig G. Merrett, and Derek Hickson for their time, guidance and patience to help make this thesis a reality.I thank the individuals within my sponsoring organization, Minto Communities, as well as within Minto Corporate Services and the Minto Sustainability
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".