Probabilistic description of short-term cloud dynamics from rapid sampling of the solar spectral irradiance
Bibliographic record
Abstract
Solar irradiance variability due to stochastic cloud dynamics can cause unwanted fluctuations in the output voltage of photovoltaic (PV) modules. These dynamics must in particular be understood at very-short and short time scales if grid interconnection and generation/load balance requirements are to be maintained for PV distributed across the grid edge. Using a recently-created database for Ottawa, Canada, a 6-month longitudinal study was conducted with a specific focus on cloud dynamics. A spectral pyranometer was used to derive full-range spectral and broadband global horizontal irradiance under all sky conditions every 250 ms. Exploiting the infrared (IR) measurement channel of this software-augmented multi-filter radiometer allowed the cloud dynamics to be probed across time scales ranging from the subsecond to minutes. Seven distinct sky conditions were self-consistently determined without sky imaging. Probability distributions, established via kernel density estimates (KDE), allowed the statistical dependence of these conditions on the spectral clear-sky index to be found. The stochastic nature of the spectral irradiance variability was probed using spectral clear-sky index increments, over time steps that were found to span three distinct variability regimes.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".