Ultra-short-term Wind Power Forecasting Based on ICEEMDAN-Informed BiGRU Network with Multi-head Attention
Bibliographic record
Abstract
To improve the ultra-short-term forecasting accuracy of wind power series, this paper proposes a combined forecasting model—ICEEMDAN-Informer-BiGRU-Attention—that integrates improved modal decomposition with deep learning modeling to address its strong non-stationarity and complex temporal structure. This model first decomposes the wind power series using an improved ensemble empirical mode decomposition (ICEEMDAN) algorithm to obtain multiple subsequences with clear frequency domain features and reduced volatility. It then employs an informer architecture to capture long-term dependencies within the series, introduces a bidirectional gated recurrent neural network (BiGRU) to model short-term dynamic features, and incorporates a multi-head self-attention mechanism to further enhance the representation of key time steps. Experimental results on a real wind farm dataset demonstrate that the proposed model outperforms mainstream models such as LSTM, GRU, and informer in terms of RMSE, MAE, and R². Its predictions are closer to real data, with more concentrated residual distributions, resulting in optimal overall performance. This validates the model's effectiveness and engineering feasibility in wind power forecasting scenarios.
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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) | 0.002 | 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".