Electricity load demand forecasting using exponential smoothing methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper five exponential smoothing methods are considered for load forecasting for lead times from a half-hour-ahead to a year-ahead. Forecasting load demand with high accuracy is required to prevent energy wasting and system failure. For this purpose, a half-hourly load demand of Malaysia for one year, from September 01, 2005 to August 31, 2006 was used. The mean absolute percentage error (MAPE) is used for comparing the forecasting accuracy. Time series of load demand recorded at half-hourly intervals contains more than one seasonal pattern, which are the intraday and intraweek seasonal cycles. The forecasts produced by the Holt-Winters Taylor (HWT) exponential smoothing outperformed the traditional Holt-Winters and modified Holt-Winters exponential smoothing methods.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it