Solar Generation Potential of Residential Neighbourhoods in an Urban Area of Downtown Toronto
Bibliographic record
Abstract
Pollution, energy security and climate change are the drivers that put into motion the need to shift to renewable energy sources from fossil fuels. Arguably one of the most promising and reliable renewable sources of energy available today is the electricity generated from photovoltaic solar panels (PV modules). This research quantifies the amount of electricity that can be generated within two 100 years old Cabbagetown residential neighbourhoods in Toronto. Renewable energy sources have relatively low geographic density and at most times are spread out over large areas sparsely. Therefore, implementation of solar PV modules in urban areas, where the land price is more with higher opportunity costs, are the perfect places to promote such solar technologies. This research study used cloud-based solar design software called Helioscope to calculate the total solar electrical production of the homes that represent the neighbourhood. Furthermore, through national resources of Canada data’s the total energy consumption of the same analyzed houses was calculated to analyse how much of the typical electricity use can be offsetted through roof top solar generation. Additionally, limiting factors like shade from trees and surrounding buildings, solar irradiance values, and orientation of the rooftop PVs were analyzed to see how they affected the solar potential. This analysis provides a better understanding of the potential of solar energy generation in the existing, established urban areas such as Cabbagetown in Toronto, and the methodology can act as a template for future studies in other places in Canada as well as internationally. .
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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 | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 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".