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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".