Optimization of Neighborhood Form for Maximizing PV Production and Potential for Transformation Into a Net-Zero Energy Neighborhood
Bibliographic record
Abstract
In the context of targets that we need to reach in order to curb the climate change, this research aims to understand if renewable energy has ability to meet the current energy demands. To determine this, a downtown area identified by the Toronto 2030 Platform was simulated by estimating rooftop PVs, considering six building typologies. The result from this simulation gave an overview of the energy generation potential in the City of Toronto. Four basic steps were followed to complete this study: first was to select six representative building-type communities; simulating those communities for their annual energy production; estimating annual energy production by PV modules within the selected area and, finally, suggesting strategies to achieve a Net-Zero energy neighborhoods. The methodology adopted for this study can be used as template for estimating PV modules energy production in other areas of the city.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".