Wind power forecasting: A hybrid multi-layer perceptron framework with adaptive noise reduction and error correction
Bibliographic record
Abstract
The increasing penetration of renewables introduces unprecedented volatility into modern power systems. Conventional forecasting frameworks often treat residual variations as unstructured noise, discarding them after correction. These approaches neglect the physical reality that residuals capture short-term disturbances, intermittency effects, and hidden fluctuations that directly affect grid stability and reliability. In this work, we propose a high-order Kalman filtering framework in which residuals are explicitly modeled as dynamic states with their own stochastic evolution. Rather than being treated as disposable errors, residuals are elevated to predictive components, enabling a simultaneous decomposition of system behavior into long-term operational trends and fast-changing renewable-driven fluctuations. The framework integrates innovation-driven covariance adaptation, allowing the filter to continuously recalibrate its process and measurement uncertainties under nonstationary grid conditions (e.g. fluctuating wind power, sudden load changes). In addition, a dual-stage neural network architecture is introduced to capture the smooth trajectory of the system state, and model high-frequency corrections. A real-time adaptive weighting strategy balances their influence, ensuring robustness both in stable operation and during disturbances triggered by renewable variability. Extensive simulations on wind power and speed datasets validate the effectiveness of the proposed method. The framework reduced mean absolute error (MAE) from 0.82 (trend based-multilayer perceptron, TMLP) and 0.88 (residual-based multilayer perceptron, RMLP) to 0.48 on the test data, representing over 40 % improvement. On the test wind speed dataset, MAE was reduced from 0.92 (TMLP) and 0.98 (RMLP) to 0.81, corresponding to gains of 14.68 % improvement.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".