AI Data Driven Approach-Based Endogenous Inputs for Global Solar Radiation Forecasting
Bibliographic record
Abstract
Controlling the random nature of renewable energy sources such as solar radiation at ground, allows electric grid operators to better integrate it. In this paper, an intelligent datadriven model based on artificial neural network with autoregressive input sequence is developed to forecast the global solar radiation (GSR) time series on a half hour resolution in the site of Agdal, Marrakesh, Morocco. The database that is used to create this model was divided into two subsets. The first subset is used for training the proposed model on the data measured during the year 2008 by adopting three efficient optimizers (levenbergmarquardt, resilient backpropagation, and scaled conjugate gradient). The second subset is used for testing the efficiency and the robustness of the developed model to generate accurate predictions during the next six years (from 2009 to 2014). The obtained results demonstrate the accuracy and the stability of the proposed data-driven model to perform prediction in case of GSR measurements intermittence or sensor damage.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".