Dynamic stacking ensemble hybrid model for enhanced short-term photovoltaic power forecasting with self-organizing maps and advanced deep learning
Bibliographic record
Abstract
To address the challenges posed by the inherent volatility of photovoltaic (PV) power generation on grid stability, this paper introduces the Dynamic Stacking Ensemble Hybrid Model (DSEHM). By integrating hybrid deep neural networks, attention-based mechanisms, tree-based models, and dynamic model selection, DSEHM enhances the prediction accuracy of non-stationary PV power series. Key components include advanced models such as Informer, Attention-Enhanced Gated Recurrent Unit (AttnGRU), and Temporal Convolutional Network (TCN). Dimensionality reduction is performed using a Self-Organizing Map (SOM), preserving topological relationships, while Gaussian Mixture Models (GMM) clustering aids in selecting optimal models for stacking. Experimental results demonstrate that DSEHM outperforms standalone models, achieving significant improvements in prediction accuracy. For instance, in the Austria dataset’s 1-step forecast, Mean Absolute Error (MAE), Relative Squared Error (RSE), and Root Mean Squared Error (RMSE) decreased by 22.55 %, 36.73 %, and 19.01 %, respectively. Furthermore, a comparative study with previous approaches further validates the effectiveness of DSEHM. These findings highlight DSEHM’s potential as a robust tool for improving PV power forecasting, with broader implications for renewable energy grid integration.
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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 | 0.001 | 0.001 |
| 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".