Estimation of Global Horizontal Irradiance (GHI) From Goes-16 Data for Hydro-Québec Operations in an Energy Transition Context
Bibliographic record
Abstract
In the context of climate change, it is important to take concrete action in terms of energy transition, and it is with this in mind that Hydro-Québec has developed technological solutions for observing and forecasting global horizontal irradiance (GHI) using GOES satellite data and semi-empirical and deep learning methods. In particular, the solutions developed will meet the company's needs in terms of solar production and demand forecasting (load forecasting), to better balance the different sources of production, as well as better optimize the reduction of demand linked to passive heating. Initial results are encouraging and show definite potential for near-real-time characterization of the solar resource, with RMSEs between 80 and 130 W/m2depending on climate type (arid vs. continental) and the method adopted (AI vs. semi-empirical algorithm).
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".