Improving short-term wind speed forecasts using regime-switching spatio-temporal covariance models
Bibliographic record
Abstract
This paper presents a methodology for forecasting short-term wind speed over a broad geographical area using regime-switching covariance models. We establish a theoretical framework for a regime-switching covariance model where the predominant wind speed and direction alter the space–time asymmetry in wind speed covariances. Using a Markovian framework in time, we define regime-specific dynamics in such a way that the long-term statistics in each regime agree with a particular parametric covariance function. A new form of the Lagrangian covariance function is proposed to incorporate prevailing wind dynamics into the wind process. Furthermore, by working with a discrete domain in space, we validate the positive definiteness constraint of the covariance functions numerically, which widens our choices for candidate functions since we are not restricted to those that are theoretically established to be positive definite. Our methodology is applied to predict wind speed up to 6-h ahead at 131 weather stations across Alberta, a region spanning over 660 000 km2. Results from a 2-year test dataset show that incorporating prevailing wind speed and direction improves forecasts in areas with consistent wind patterns. In regions with more complex wind dynamics, a symmetric model with relaxed parameter constraints performs better. This approach provides a flexible and scalable framework for short-term wind forecasting, particularly useful for large-scale wind energy applications.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".