A New Closed-Loop Solar Power Forecasting Method With Sample Selection
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
In this article, a new short-term solar power forecasting method is proposed which has a closed-loop structure composed of point-estimating and range-classifying parts. If the forecasts generated by these parts for solar power are inconsistent, the feedback loop sends appropriate signals to them to correct their predictions. The feedback loop iterates until consistent forecasts are generated for the solar power by the point-estimating and range-classifying parts. This enables the proposed closed-loop forecasting method to enhance its solar power prediction accuracy and reliability. Furthermore, a novel sample selection approach, different from feature selection methods, is devised to mine the historical data for finding the most informative training samples for training the proposed forecasting engine. The effectiveness of the proposed solar power forecasting method is illustrated by testing it on some real-world solar farms and comparing its results with the results of several state-of-the-art solar power prediction methods.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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