Modeled estimates of solar direct normal irradiance in Al-Hanakiyah, Saudi Arabia and Boulder, USA
Bibliographic record
Abstract
Accurate knowledge of the solar resource is required for solar energy generation projects, including preferably data for both the direct normal (DNI) and diffuse horizontal (DHI) components of irradiance, but in many solar datasets, only a single global horizontal (GHI) may be available. This paper undertakes the evaluation of three models-Reindl*, BRL and DISC - which estimate DNI and DHI in such cases. The models are tested against actual data from Boulder, USA, and Al-Hanakiyah, Saudi Arabia. Generally, a good correlation between hourly measured or calculated DNI and hourly modeled DNI is observed for the three models in Boulder and Al-Hanakiyah. The three models overestimate DNI in Al_Hanakiyah while Reindl* and BRL model exhibit less bias for estimating DNI in Boulder. The results show that the BRL model outperforms other models in Boulder with RMSE of 121 W/m2and standard deviation (a) of 121 W/m2. Overestimation of DNI has been noticed in BRL and Reindl* models with values that exceed 1100 W/m2. On the other hand, Reindl* model's performance is better with MBE of -28 W/m2, MAE of 63 W/m2, and RMSE of 86 W/m2in Al-Hanakiyah. Other locations such as Ottawa, Canada and Ma'an, Jordan as well as DIRINT model will be included to evaluate the performance of each model under different weather conditions in the future.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".