Benchmarking on improvement and site-adaptation techniques for modeled solar radiation datasets
Bibliographic record
Abstract
High-accuracy solar radiation data are needed in almost every solar energy project for bankability. Time series of solar irradiance components that spans decades can be supplied by satellite-derived irradiance or by reanalysis models, with very various types of uncertainty associated to the specific approaches taken and quality of boundary conditions information. In order to improve the reliability of these modeled datasets, comparison with ground measurements over a short period of time can be used for correcting some aspects, bias mainly, of the modeled data by using different methodologies; this procedure is known as site adaptation. Therefore, a benchmarking exercise that uses different site adaptation techniques was proposed within the Task 16 IEA-PVPS activities. In this work, over ten different site-adaptation techniques have been used for assessing the accuracy improvement, using ten different datasets covering both satellite-derived and reanalysis solar radiation data. The effectiveness of these methods is found not universal or spatially homogeneous, but in general, it can be stated that significant improvements can be achieved eventually in most sites and datasets.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".