Short-term wind speed forecasting with regime-switching and mixture models at multiple weather stations over a large geographical area
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
This paper presents a methodology to incorporate large-scale atmospheric information into short-term wind speed forecast over a large geographical area of about 435 000 square kilometers in Alberta, Canada. The analysis was done using two publicly accessible datasets. The ERA5 reanalysis dataset is used for atmospheric clustering by applying the k-means algorithm and the hidden Markov model on atmospheric variables related to wind speeds. It is shown that atmospheric clustering results align with some known wind patterns in Alberta. For short-term wind forecast, we propose time series regime-switching models and mixture models that integrate the clustering results to predict 6-h ahead wind speed at 23 weather stations in Alberta, Canada. The predictive performance is compared for atmospheric clustering methods and forecasting models. The results show that models that take into account meteorological conditions perform better than those do not. Furthermore, modeling multiple locations simultaneously produces fewer forecasting errors than modeling at a single location.
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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 | 0.001 | 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