Forecasting Green Energy Production in Latin American Countries and Canada via Temporal Fusion Transformer
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT Forecasting green energy is crucial in diminishing dependence on fossil fuels and fostering sustainable development. However, it encounters notable challenges, such as variable demand, restricted data availability, the integration of various datasets, and the necessity for precise long‐term projections. This study thoughtfully examines these issues using the temporal fusion transformer (TFT) model to project green energy production across five Latin American nations (Argentina, Brazil, Chile, Colombia, and Mexico) and Canada, drawing on data from 1965 to 2023. The performance of the proposed TFT is more authentic as compared with the gated recurrent unit (GRU), the long short‐term memory (LSTM), deep autoregression (DeepAR), and the meta graph‐based convolutional recurrent network (MegaCRN). The TFT has a mean square error (MSE) of 0.0003, root mean square error (RMSE) of 0.0173, mean absolute error (MAE) of 0.0112 and mean absolute percentage error (MAPE) of 1.76%. From the preceding results, it is clear that the proposed TFT model can identify dynamic energy patterns that will contribute towards achieving sustainable development goals by the end of 2040.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it