SARIMA Model Forecasting of Short-Term Electrical Load Data Augmented by Fast Fourier Transform Seasonality Detection
Bibliographic record
Abstract
In this study, Seasonal Autoregressive Integrated Moving Average (SARIMA) model is used to forecast short term electric load data. It is known that electrical load data is affected by weather conditions, therefore the electrical load data will have a seasonal component. For this reason, representing this data in the frequency spectrum domain will reflect the exact seasonal time. As such, Fast Fourier Transformation algorithm (FFT) has been used to detect the existence of the seasonal component in the time series of hourly electrical load data. The FFT technique gives a clear view of the behavior of the time series, which shows the three main components located at frequencies f1= 3.17 e-8Hz, fz=1.157e-5Hz, and f3=2.315 e-5Hz. The component that is located at f2=1.157e-5Hz has a significant amplitude and tends to repeat itself every 24 hours. The model that has been selected to forecast one week ahead of the electrical load data is SARIMA (6,1,1) (4,1,1). Most of the predicted values fall into the 95% of confidence interval. The P-values for Ljung-Box statistic indicate that of P-values are higher than 5%. Unlike other techniques, FFT technique offers a quick view of the data behavior with high accuracy.
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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 | 0.000 |
| Research integrity | 0.000 | 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".