Wind Speed Forecasting using Machine Learning Approach based on Meteorological Data-A case study
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a forecasting method to anticipate wind speed accurately. This method is applied to an energy production site using an artificial intelligence method based on machine learning. The accuracy of this method is higher compared to other existing methods in the literature for time series analysis such as artificial neural networks and the complexity of wind speed prediction models without loss of information content. We tested several thousand data between 1985 and 2018 in the city of Basel, a city in northwestern Switzerland. We have used the MATLAB Software for this modeling. The study demonstrates that the use of statistical models based on machine learning is relevant to predict of speed and direction of the wind in power generation systems from meteorological data. The results obtained are presented and discussed.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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