A deep‐learning based solar irradiance forecast using missing data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Irradiance prediction is a vital task in the renewable energy field. Its aim is to forecast the irradiance or power of a photovoltaic plant and thus provide a reference for stabilizing the power grid. In the real scenarios, missing data can significantly reduce the accuracy of the prediction. Meanwhile, due to the unawareness of the distribution of datasets, it is difficult to choose a suitable imputation method before modeling. Also, different imputation methods do not have the same good effects on different datasets. In this article, a recurrent neural network with an adaptive neural imputation module is proposed for forecasting direct solar irradiance using missing data. The model predicts future 4‐h irradiance based on the missing historical climate and irradiance data without imputing the data in pre‐processing stage. The proposed model is tested on the open access datasets, with missing values generated randomly in all input series. The model performance is compared under various missing rates and different input factors with other imputation methods. The results demonstrate that the proposed methods outperform other methods under different evaluation metrics. Furthermore, the authors integrate the model with the attention mechanism and find it has better performance at high irradiance.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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