Spectral irradiance correction of photovoltaic energy yield predictions in six high-latitude locations with measured spectra
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As solar power is expected to supply over 16% of the world’s electricity mix by 2030, accurate energy yield prediction and forecasting is essential. The incident solar spectrum varies continuously, causing energy prediction error as sunlight deviates from the reference spectrum. This work quantifies spectral error in bifacial photovoltaic energy yield predictions, demonstrating instantaneous spectral impacts of −45% to +32% and annual impacts from +0.7% to +2.7% on energy yield. This work presents solar spectral impact analysis for seven North American locations (39.7–69.1°N). We apply measured spectra to bifacial fixed-tilt silicon modules using a 2D view-factor model. Ground-reflected and diffuse irradiance cause the largest spectral errors while direct irradiance is best-matched to reference conditions. This study indicates that spectral correction methods should be applied for bifacial systems or in locations with high diffuse fractions above 35%, representing over 75% of Earth’s landmass. These effects indicate current PV system designs underestimate diffuse and ground-reflected irradiance contributions.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it