Hybrid Models for Estimating 5-Minute Global Solar Irradiance on an Inclined Surface: A Case Study on Two Regions in Algeria
Bibliographic record
Abstract
Assessing solar energy potential is vital for developing solar conversion technologies.Despite Algeria's high solar capacity, the country faces challenges due to a limited number of meteorological stations that measure solar radiation (SR).This paper presents a study investigating the performance of innovative hybrid models (HMs) proposed to improve the SR estimation on inclined surfaces over 5-minute intervals.These HMs are formed by combining five empirical models (EMs) and five transposition models (TMs), resulting in a total of 25 models.The 25 HMs are applied to estimate the SR in two locations in Algeria, Bouzareah and Ghardaia.A comparative study is conducted in MATLAB, evaluating the performance of the suggested HMs and five EMs.The findings demonstrate that the HMs significantly enhance accuracy, particularly under cloudy conditions, reducing the normalized root mean square error (NRMSE) by up to 90% in some cases.For example, on August 16 th in Ghardaia, the NRMSE decreased from 25% to 5.32% with our hybrid technique, demonstrating its superiority.Unlike traditional clear-sky models, our method performs well on overcast days.For example, on December 10 th in Ghardaia, the Bird and Hulstrom model alone produced a high NRMSE of 35% with a KT value of 0.28; however, combining it with the Temps model reduced the NRMSE to 13.37%.In addition, the HMs based on Bird & Hulstrom-Temps provide the most accurate estimates at both locations, with coefficient of determination (R² ) values from 0.9788 to 0.9992 and NRMSE values between 3.33% and 19.64%.In contrast, the Davis and Hay-Hay-based HMs offer the lowest R² values and the highest NRMSE and NMBE values.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".