Short-Term Load Foresting Using Combination of Linear and Non-Linear Models
Bibliographic record
Abstract
Numerous short-term load forecasting models are available in the literature. However, the improvement in forecast accuracy using the combination models has yet to be analyzed on a daily rolling basis for a very long test period. In this paper, the characteristics of a combination of the Seasonal Autoregressive Integrated Moving Average (SARIMA) – a linear model and Radial Basis Function networks (RBFN) – a non-linear model have been studied in two different modeling frameworks namely single series (SS) and variable segmented series (VSS). The hourly load data of the Ontario Electricity Market (OEM) and the Iberian Electricity Market (MIBEL) are used as the case system to produce the daily load forecast. The impact on prediction accuracy by the size of training data and the combining individual forecasts has been studied for 12 years of OEM and one year of MIBEL demands, respectively. In order to achieve the empirical objective, a large number of models(1,447,740 in number) are estimated to produce load forecasts on a daily rolling basis. The forecast performance has been compared with the other models proposed in the literature.Among the linear models, for all window sizes of training data, the forecast accuracy of the combination model is better than the model selected with the minimum Akaike information criterion (AIC) and Bayesian information criterion (BIC) in both frameworks. Moreover, the ensemble of RBFN and linear models produces the best forecast. The results pinpointed that the proposed model’s precision and stability are higher than the earlier forecasting models proposed for both markets. The novelty in the model is that only a single hourly time-series is used for forecasting and there is no need for other explanatory variables.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".