Deep Learning Approach for Solar Irradiance Forecasting: A Moroccan Case Study
Bibliographic record
Abstract
Due to its influence on applications such as renewable energy generation, solar irradiance data, and meteorological parameters have risen in prominence. However, developing an accurate model for predicting solar irradiance based on multiple weather parameters remains a challenging issue. As a novelty, a multi-horizon forecasting scheme ranging from 1 to 3 days ahead is studied in the present work. A SeqtoSeq model architecture to forecast global horizontal irradiance based on Masen’s dataset. Univariate and multivariate SeqtoSeq models are implemented to forecast global horizontal irradiance (GHI) based on Masen’s dataset. Moreover, the performance of the proposed models was compared against other models such as Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM). The obtained results reveal that the proposed multivariate model outperforms all the other models and improves prediction in terms of Mean Absolute Error (MAE) by 42.49, 48.29, and 47.78% for 1 day ahead, 2 days ahead, and 3 days ahead, respectively.
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How this classification was reachedexpand
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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.062 | 0.122 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.001 | 0.009 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".