Simulation of Tilted Rooftop Photovoltaic Panels at City Scale: Novel Measurements, Model Development, and Application in WRF
Bibliographic record
Abstract
Abstract Rooftop photovoltaic (PV) panels alter the urban energy balance and affect local climate. However, the use of simplified PV models and models lacking thorough evaluation against observational data has resulted in conflicting conclusions related to their local climate impacts. Here, we further develop a rooftop PV energy balance model, UCRC‐Solar, and couple it to the multilayer urban canopy scheme BEP‐BEM in the Weather Research and Forecasting (WRF) model. Model extensions include updated radiative and convective energy exchanges between both sides of the PV module and the atmosphere/roof surface. We conduct a year‐long measurement campaign in London, Canada, to provide a comprehensive meteorological and energy balance data set for an array of tilted PV panels on a flat roof. The upgraded UCRC‐Solar is evaluated extensively against this newly collected PV module surface temperature and electricity production data, both offline and online. Coupled mesoscale WRF simulations for Toronto, Ontario, showcase the impacts on urban climate from different configurations of rooftop PV models. UCRC‐Solar with tilted panels shows the most notable daytime warming (C) and the least nighttime cooling (C) of the near‐surface air temperature, followed by UCRC‐Solar with flat panels, and finally, the existing WRF PV model, which yields more cooling. Unlike previous work at this scale, our approach includes all relevant physical processes and rigorous model evaluation for extended periods across different locations. Furthermore, the updated UCRC‐Solar in WRF permits panels with any tilt, which has not previously been available at this scale.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".