Assessment of SUNY Version 3 Global Horizontal and Direct Normal Solar Irradiance in Canada
Bibliographic record
Abstract
In this paper, hourly, daily and annual solar resource data derived form the latest SUNY solar model (version 3) using visible and infrared satellite data is analysed and compared with ground measured solar data from eighteen northern- latitude locations distributed all across Canada. The statistics of spatial and temporal differences between the two datasets obtained from the two versions of SUNY model, i.e., V1 and V3, are analysed for both global horizontal irradiance (GHI) and direct normal irradiance (DNI). SUNY V3 GHI and DNI data set is also compared to a dataset produced by the MAC3 cloud layer model for ten northern-latitude locations across Canada. The MAC3 model, using ground-based data, is the basis of the weather design input data files referred to in the current Canadian Model National Energy Code. It is also the model used for generating the CWEEDS (Canadian Weather Energy and Engineering Data Sets) long term hourly dataset, which is in turn used to derive the CWEC files (Canadian Weather year for Energy Calculations) also called typical meteorological years. CWEC files are used for design and analysis in various applications, including buildings heating and cooling as well as solar systems. Overall, results show that SUNY V3 has improved slightly compared to SUNY V1 in terms of estimating global and beam irradiance. Comparison of the SUNY V3 beta model with the MAC3 model seems to indicate that SUNY V3 model is resulting in better DNI estimates than those derived by the MAC3 model. Both SUNY V3 and MAC 3 models give similar estimates for GHI.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".