Optimizing rooftop photovoltaic panel configurations: Implications from long-term simulations for North American cities
Bibliographic record
Abstract
Large-scale adoption of rooftop photovoltaic (PV) panels has been suggested as a climate mitigation strategy as well as a local heat adaptation strategy since PVs provide shade to the underlying roof surface and simultaneously generate electricity to supply indoor cooling energy. However, PVs can potentially exacerbate daytime warming as a large fraction of incoming solar radiation is converted to heat due to limited electricity production rate, low panel albedo, and minimal panel energy storage. Currently, commercially available PV modules have an electrical energy conversion efficiency of ~ 20%, while PVs with an efficiency up to 50% have been demonstrated in a laboratory. With anticipated advances in PV materials and efficiency, it is important to examine how different PV properties would impact local climate, and assess opportunities for minimization of the potential daytime warming.In this study, we couple the newly updated and evaluated rooftop PV model, UCRC-Solar, to the single-layer urban canopy model, Town Energy Balance (TEB), and explore various configurations of rooftop PV panels under various climatic conditions for major cities in North America. In particular, long-term offline TEB simulations are conducted for the current climate, driven by the ERA5-Land reanalysis. We investigate how different PV energy production efficiencies, tilt angles, surface emissivities, and panel spacing affect the surface temperature of the roof and the PV modules and sensible heat flux from the roof, PV panels, and the roof-PV systems. Furthermore, an online WRF case study is conducted for Toronto, Canada, under the RCP8.5 scenario with optimized PV configurations to better assess PV impacts on the local climate.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".