Energy Districts: energy efficiency evaluation and solar strategies for representatives’ Canadian neighbourhoods (Canadian Archetypes)
Bibliographic record
Abstract
This thesis examines the energy performance of existing Canadian neighborhoods using a method that analyzes the urban layout and multiple archetypes that are representatives of urban communities in Canada. The method presented uses different tools to assess and model the buildings of urban areas using assumptions based on census data and energy consumption databases. The locations selected to develop this study are Richmond, BC, Calgary, AB, Winnipeg, MB, Toronto, ON, and Montreal, QC. Utilizing commonly software used for energy simulation like SketchUp and EnergyPlus, energy models are proposed to estimate the energy consumption of each neighborhood and compared to a baseline developed using the NRCan database for average energy use per end-use. After achieving the energy demand of each urban zone, this thesis proposes different material composition for the building envelope by applying highly insulated materials and analyzes the impact of it I with respect to energy consumption. Focusing on achieving net-zero energy, solar strategies are also hypothetically implemented to offset the rest of the energy usage. Results shows that neighbourhoods with a conventional grid and tilted orientation presents a better energy reduction due to the better solar potential and geometric layout, where a neighbourhood met 95% of its energy needs with the proposed 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".