Short-Term Wind Power Forecasting under Data Loss Conditions: A Case Study in Vietnam
Bibliographic record
Abstract
The effective utilization of wind energy strongly relies on accurate power forecasting, in which short-term prediction plays a crucial role in grid operation.In practice, measurement data are often incomplete due to data loss.Existing studies typically address this issue by either focusing on data imputation, developing forecasting models based on deep learning or machine learning (DL/ML), or integrating numerical weather prediction (NWP) models and data security.However, only a limited number of approaches effectively combine robust imputation with powerful time-series forecasting models, while also ensuring comprehensive evaluation under various data loss scenarios and maintaining both interpretability and practical applicability.Moreover, regional characteristics significantly influence forecasting methods and wind power management strategies.To address these challenges, this paper proposes a hybrid XGBoost-GRU model for short-term wind power forecasting, with a case study in Southern Vietnam.Experimental results demonstrate that the proposed model outperforms the baseline GRU model by achieving higher predictive accuracy and maintaining stable performance under different data conditions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".