Cloud cover-based models for estimation of global solar radiation: A review and case study
Bibliographic record
Abstract
Solar radiation data are essential information for designing and studying various engineering systems such as building thermal performance, photovoltaics, solar thermal systems, and passive solar design. Several empirical models have been developed to simulate the available solar radiation on the earth’s surface. This article presents a comprehensive review of cloud cover-based solar radiation models (CSRMs) for estimating the hourly global solar radiation (GSR). Many of these models have been developed according to the Angstrom–Prescott relationship for solar radiation and are mainly used to estimate the monthly average daily total solar radiation. Based on the comprehensive review of CSRMs, it could be stated that the Kasten-Czeplak model and the Lam-Li model are comparatively straightforward and precise for estimating the hourly GSR. Furthermore, the study evaluated the selected model (Lam-Li model) to estimate the hourly GSR on the horizontal surfaces for four different cities in Canada. Results revealed that the original Lam-Li model performs within an acceptable range based on the statistical indices (R2 = 0.77–0.80 and rRMSE = 39.7–44.0%); however, the performance of the newly modified model (M2) from this study performs significantly better (R2 = 0.8–0.82 and rRMSE = 3.65–39.5%) than the original model (M1).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".