A Multi-Phase Deep Learning Framework for Multi-Step Short-Term Wind Power Forecasting in Presence of Uncertainties
Bibliographic record
Abstract
With the expanding share of wind energy in power grids, accurate forecasting has become critical for maintaining system stability and operational efficiency. Notwithstanding, forecasting accuracy is compromised by uncertainties from fluctuating wind speeds and meteorological conditions. This paper proposes a novel multi-phase short-term wind power forecasting framework (multi-step ahead forecasting over a 1-hour horizon). Thus, decomposition of the wind power signal and feature extraction are initially implemented using Variational Mode Decomposition (VMD) and Principal Components Analysis (PCA), respectively, aiming to enhance input quality and reduce computational burden. The proposed forecasting model is built on a hybrid DL architecture merging a Convolutional Neural Network (CNN), Attention Mechanism (AM), and Deep Feedforward Neural Network (DFFNN). Given the impact of decomposition levels and extracted PCA components count on forecasting performance, a search-based scheme is developed to explore a pre-defined space (maximum decomposition level and extracted components count) to determine the optimal configuration for each interval. In the next phase, a Fuzzy Decision-Making (FDM) technique is employed to select a balanced and optimal configuration for the proposed model across the year. To demonstrate the proposed architecture’s efficacy and generalizability, the model is tested on two real-world data from La Haute Borne wind farm in France and Hill of Towie wind farm in Scotland. Results demonstrate that the proposed architecture with the selected configurations achieves significant accuracy and generalization, with average NRMSEs and NMAEs values of 0.428% and 0.333% for La Haute Borne wind farm and 0.502% and 0.381% for Hill of Towie wind farm.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".