A novel <scp>VMD</scp> ‐ <scp>LassoNet</scp> ‐ <scp>iTransformer</scp> framework with enhanced feature fusion for dynamic <scp>NOx</scp> forecasting in flexible utility boilers
Bibliographic record
Abstract
Abstract Accurate prediction of NOx emissions in flexible utility boilers is essential for both environmental protection and operational optimization. This study proposes a novel variational mode decomposition (VMD)‐LassoNet‐inverted Transformer (iTransformer) framework for dynamic NOx forecasting. First, an improved VMD with adaptive thresholding is used to reduce the noise in the original data. Next, a LassoNet network with L1‐regularized skip‐layer connections is utilized for feature selection in the first stage, reducing input dimensions from 33 to 28 critical parameters while maintaining interpretability. In the second stage, the iTransformer model performs deep feature extraction using a temporal attention mechanism, enabling accurate dynamic modelling of NOx emissions by capturing multivariate correlations and long‐term dependencies. Experiments on a 660 MW utility boiler dataset demonstrate that the proposed model achieves state‐of‐the‐art performance, achieving an R 2 value of 0.95, a MAE of 2.63 mg/m 3 , and a RMSE of 3.71 mg/m 3 in single‐step forecasting on the test data, surpassing LSTM, Transformer, and TimesNet. In multi‐step forecasting, the model reduces prediction errors by 7.46% and 3.4% compared to Transformer and TimesNet, respectively. The framework's hierarchical feature fusion and temporal inversion mechanisms effectively handle load‐varying scenarios, providing a solid foundation for real‐time NOx emission control.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".