Impact of measured spectrum variation on solar photovoltaic efficiencies worldwide
Bibliographic record
Abstract
In ratings of solar photovoltaic performance, variation in the spectrum of sunlight is commonly neglected. A single spectrum, AM1.5, is used as the sole basis not only for record laboratory efficiencies, but also for commercial module power ratings, the performance metrics for solar power plants, and warranty claims. Incorporation of solar spectrum variation would improve accuracy and reduce the financial consequences of prediction errors. Ground-level measurements of spectral irradiance collected worldwide have been pooled to provide an extensive – though by no means comprehensive – sampling of the variation. Applied to nine solar cell types, the resulting divergence in solar cell performance illustrates that a single spectrum is insufficient for comparison of cells with different spectral responses. In contrast with single-junction cells such as silicon and cadmium telluride, cells with two or more semiconductor junctions tend to have efficiencies below that obtained under AM1.5. Increases in the degree of sun tracking are shown to decrease efficiency for cells with a narrower spectral response. Of the nine cell types, silicon exhibits the least spectral sensitivity: relative site variation ranges from 1% in Lima, Peru to 14% in Edmonton, Canada, with a mean of 4%.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".