Wind power forecasting using wavelet transforms and neural networks with tapped delay
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
With an objective to improve wind power estimation accuracy and reliability, this paper presents Linear Neural Networks with Tapped Delay (LNNTD) in combination with wavelet transform (WT) for probabilistic wind power forecasting in a time series framework. For comparison purposes, results of the proposed model are compared with the benchmark model, different neural networks and WT based models considering performance indices such as accuracy, execution time and R2statistic. For the reliability and proper validation of the proposed model, this paper highlights the probabilistic forecast attributes at different skill tests. The historical data of the Ontario Electricity Market (OEM) for the period 2011-2014 were used and tested for two years from November 2012 to October 2014 with one month moving window considering all seasonal aspects. The experimental results clearly show that the results of the proposed model have been found to be better than others.
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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