Self-Sufficiency Building Energy Modelling from Urban to Block-Scale with PV Technology
Bibliographic record
Abstract
Decarbonisation policies are often implemented in cities through the promotion of rooftop solar resources.However, urban solar assessments need to identify favourable locations and appropriate sizing to effectively support these strategies.This research aims to estimate the potential for photovoltaic (PV) systems in a dense urban context, as a basis for future policy support.The downtown district of Toronto, Ontario (Canada) is examined as a case study using the 2030 online platform.This work adopts a multi-scalar methodology to model the potential of roof-mounted PV systems for the main residential archetypes.An urban-scale GIS-LiDAR assessment, informed by environmental criteria, is followed by a block-level optimization using URBANopt, which considers energy and economic parameters.The rooftop GIS-based analysis estimates that up to 20% of electricity consumption for detached houses could be satisfied, primarily in the summer, and 5% for apartment buildings.Optimization with URBANopt shows that solar collective configurations can provide significant benefits to users, primarily in terms of economics.When optimization is performed by clusters for each block, the benefits over single-building analysis are evident, particularly in reducing lifecycle costs.In the selected case study, polycrystalline panels with net metering can achieve self-sufficiency levels ranging from 18% to 41% for residential blocks.This study confirms that solar PV systems can increase local production, reduce grid energy dependency, and support energy communities.
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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 | 0.001 |
| 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".