Urban-scale modeling of building energy self-sufficiency using rooftop photovoltaics
Bibliographic record
Abstract
Buildings are major contributors to global energy-related CO2 emissions, accounting for a significant share of global climate impacts. This has highlighted the critical need to transition toward a carbon-neutral building stock by 2050. Rooftop photovoltaics (PVs) offer substantial potential to reduce energy demand and enhance urban energy self-sufficiency. This work focuses on developing an improved CityBEM framework, an in-house urban building energy model (UBEM), to evaluate the role of rooftop PV systems in decarbonizing urban energy systems. The enhanced methodology enables city-scale, high spatiotemporal resolution simulations of both building energy use and rooftop PV retrofitting while addressing key computational and data limitations commonly faced in UBEM applications.CityBEM’s robust and scalable approach allows for transient simulations of individual buildings with diverse usage types, making it applicable to large urban areas. The rooftop PV module incorporates physics-based modeling, validation, and optimized designs to account for self-shading effects and maximize energy generation potential.Currently, the tool is being applied to model the entire city of Montreal. The goal is to generate high spatiotemporal resolution simulations of energy demand and on-site electricity generation from rooftop PVs. This framework aims to provide actionable insights into the role of rooftop photovoltaics in achieving cleaner, energy self-sufficient cities and informing strategies for large-scale urban retrofitting and decarbonization.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".