Machine learning and statistical approaches for wind speed estimation at partially sampled and unsampled locations; review and open questions
Bibliographic record
Abstract
Wind resource assessment (WRA) depends on the availability of accurate, long-term wind speed data. In locations where such data is limited (partially sampled locations, PSL) or completely missing (unsampled locations, USL), various physical, statistical, and machine-learning methods have been developed to address these gaps. This paper presents a comprehensive and up-to-date review of statistical and machine-learning methods for estimating long-term wind speed at PSLs and USLs. It was found that the “Measure Correlate Predict” (MCP) is still the method of choice for estimation at PSL. However, this approach has evolved with the adoption of machine learning, especially Artificial Neural Networks, and reanalysis wind data as the reference site. In general, reanalysis datasets have seen growing adoption for WRA at both PSLs and USLs due to their global coverage, high temporal resolution, and demonstrated accuracy. At USLs, uncorrected and bias-corrected reanalysis wind speed data are used for WRA, with the Global Wind Atlas predominantly used to correct reanalysis wind speed data. There is also a growing effort to develop machine learning models, including deep learning models for reanalysis bias correction at USLs using explanatory variables derived from high-resolution topographic and land use datasets. Challenges to estimating long-term wind speed at PSLs and USLs are identified and discussed: data uncertainties, disparity in the accuracy of reanalysis wind data, model transferability, and nonstationary conditions. Finally, recommendations for future research and development directions are presented, including techniques that consider documented non-stationarity in wind speed data.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".