The Limits of Forecasting: Assessing the Robustness of Time Series Models to Extreme Load Volatility
Bibliographic record
Abstract
Accurate mid-term load forecasting is indispensable for effective operational planning and asset management within electrical transmission systems.This research offers a thorough comparison of seven forecasting models-comprising one stochastic model Exponential Smoothing (ES) and six deterministic trend models (Linear, Exponential, Logarithmic, and Polynomial of Orders 2 to 4)-aimed at predicting weekly transformer load (MWh) based on supervisory control and data acquisition (SCADA) data from the 150 kV Pekalongan Substation.Model performance was evaluated utilizing established metrics (MAPE, MAE, RMSE) and was statistically validated through the Friedman test.The principal conclusion indicates that there is no statistically significant difference in performance among the models (χ² (6) = 0.25, p > 0.05).Although slight variations in metrics were observed, visual analysis confirmed consistent performance on stable data and universally indicated failure during periods of extreme volatility.These findings strongly endorse the Principle of Parsimony, demonstrating that more complex models do not yield accuracy improvements over simpler alternatives such as Linear or Quadratic models.This study offers vital guidance for utility companies, endorsing the adoption of simple, interpretable models for routine operational forecasting to enhance planning efficiency while ensuring reliability.
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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 | 0.016 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".