Comparison of Spectral Correction Methods: Spectral Irradiance Measurements and Parameterized Spectral Models
Bibliographic record
Abstract
A standard solar spectrum is assumed for photovoltaic (PV) module performance calculations, but spectral solar irradiance is continuously changing, impacting PV efficiency and energy yield. We calculate the spectral mismatch modifier (SMM) for one year of measured spectral irradiance data in Roskilde, Denmark. Irradiance wavelength measurement range affects the calculated SMM: neglecting long-wavelength irradiance above 1700 nm underestimates module current increase due to clouds and overestimates module current increase due to air mass. We also compare five parameterized spectral correction methods. Tilt of the spectral irradiance measurement (or of input training data for parameterized models) is non-negligible when calculating SMM. Parameterized methods relying on clearness index predict large positive spectral effects due to clouds, an effect that is neglected in other models. Further work is required to ascertain the most effective spectral correction approaches.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".