Probabilistic prediction intervals of short-term wind speed using selected features and time shift dependent machine learning models
Bibliographic record
Abstract
Forecasting wind speed plays an increasingly essential role in the wind energy industry. However, wind speed is uncertain with high changeability and dependency on weather conditions. Variability of wind energy is directly influenced by the fluctuation and unpredictability of wind speed. Traditional wind speed prediction methods provide deterministic forecasting that fails to estimate the uncertainties associated with wind speed predictions. Modeling those uncertainties is important to provide reliable information when the uncertainty level increases. Models for estimating prediction intervals of wind speed do not differentiate between daytime and nighttime shifts, which can affect the performance of probabilistic wind speed forecasting. In this paper, we introduce a prediction framework for deterministic and probabilistic short-term wind speed forecasting. The designed framework incorporates independent machine learning (ML) models to estimate point and interval prediction of wind speed during the daytime and nighttime shifts, respectively. First, feature selection techniques were applied to maintain the most relevant parameters in the datasets of daytime and nighttime shifts, respectively. Second, support vector regressors (SVRs) were used to predict the wind speed 10 minutes ahead. After that, we incorporated the non-parametric kernel density estimation (KDE) method to statistically synthesize the wind speed prediction errors and estimate the prediction intervals (PI) with several confidence levels. The simulation results validated the effectiveness of our framework and demonstrated that it can generate prediction intervals that are satisfactory in all evaluation criteria. This verifies the validity and feasibility of the hypothesis of separating the daytime and nighttime data sets for these types of predictions.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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) | 0.000 | 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 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".