Assessment of the Solar Resource in Andean Regions by Comparison between Satellite Estimation and Ground Measurements: Study Case of Ecuador
Bibliographic record
Abstract
To develop and implement solar technologies, it is necessary to know accurately the solar resource on the place. Consequently, several institutions working in renewable energy have made efforts to both measure solar radiation and develop models to estimate solar radiation. The main drawback with estimations is their lack of accuracy. Recently, the NREL (NSRDB) has update its meteorological database based in satellite estimations. Some validations have been reported, however, no studies about its validity for the Andean region have been done. In this work, measurements of global horizontal irradiation (GHI) from 53 stations placed along the Ecuadorian territory were compared with satellite estimation data from NSRDB. Statistical descriptive indicators of dispersion (RMSE, MBE) and goodness of fit (KS test) were used. The data were grouped in hourly, daily, weekly and monthly basis, as well as in clear and cloudy basis. Results show that monthly grouping may be used with confidence, since close to 95% of comparisons have a good fit. Also, results show that both, clear sky and cloudy models tend to overestimate solar radiation in such a region. Solar resource seems to be high in Ecuador since more than 75% of the territory has values over 3.8 kWh/m2day.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".