Internet of Energy: Ensemble Learning through Multilevel Stacking for Load Forecasting
Bibliographic record
Abstract
In the Internet of Energy (IoE) ecosystem, an accurate electricity load forecasting is critically important to all the participants in the smart grids, such as manufacturers, utility companies, renewable energy generators, consumers and prosumers. It is a vital component for a stable and reliable operation of the electricity grid, effective demand side management, and the success of energy efficiency programs. We present a novel ensemble learning mechanism through multi-level stacking for the load forecasting. Our proposed architecture has four layers utilizing strong learners at each level (base and meta) with an input features set, at all the levels, that includes initial (original) variables/features from the dataset along with meta-features extracted during stacking. We use computational intelligence (CI) techniques such as Random Forest, Cubist, k-Nearest Neighbors (KNN), eXtreme Gradient Boosting (XGBoost), Support Vector Machine Regression (SVM-R), Multivariate Adaptive Regression Splines (MARS), and Principal Component Regression (PCR) to train participating prediction models at various levels of stacking. The results show significant improvement in performance with Root Mean Square Error (RMSE) reduced by 36.81%, a 38.82% reduction in Mean Absolute Error(MAE), and R2increased from 0.787 to 0.923 when compared to single forecasting model.
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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 | 0.002 |
| 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 | 0.002 |
| Open science | 0.001 | 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".