Direct Interval Forecast of Uncertain Wind Power Based on Recurrent Neural Networks
Why this work is in the frame
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Bibliographic record
Abstract
Interval forecast is an efficient method to quantify the uncertainties in renewable energy production. In this paper, the idea of prediction intervals (PIs) is employed to capture the uncertainty of wind power generation in power systems. The recurrent neural network (RNN) model is proposed to construct PIs with the lower upper bound estimation method, which is a powerful nonparametric forecast approach. In addition, a novel comprehensive cost function with a new PI evaluation index is designed with the purpose of enhancing the model training. To tune the parameters of RNN prediction model, the dragonfly algorithm with a linearly random weight update method is introduced as the optimization tool. The performance of the proposed prediction model is validated by a case study using a real world wind power dataset, and the comparative results show the superiority of the model.
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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 | 0.000 |
| 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