A Comprehensive Analysis of a Hybrid Deep Learning Model for Midterm Electric Load Forecasting
Bibliographic record
Abstract
In the realm of time series forecasting, particularly for midterm electric load predictions, hybrid deep learning models have shown promising results. This paper introduces a thorough implementation and validation of the established ETS+RD-LSTM model. Leveraging the synergy of Exponential Smoothing (ETS) and Residual Dilated Long Short-Term Memory (RD-LSTM), the model was applied to monthly electricity demand datasets from 10 different countries. The validation process employed was the walk-forward validation method. When juxtaposed with traditional models like ARIMA, MLP, XGBoost, ETS, kNN, and LSTM, the ETS+RD-LSTM model demonstrated superior forecasting accuracy and robustness. An in-depth performance analysis offers insights into the strengths and nuances of the model's prediction capabilities. Through this research, we aim to contribute to the growing body of knowledge on advanced hybrid forecasting models and their practical applications in the energy sector.
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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) | 0.000 | 0.000 |
| 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".