An adjustment method for electricity sales forecasting result considering the effects of spring festival
Bibliographic record
Abstract
The effects of Spring Festival on electricity sales of different trades were considered and an adjustment method for electricity sales forecasting result was proposed.Firstly,the month-quarter ratio of historical electricity sales in each trade of every month in the first quarter and the distance between Spring Festival and the first day of every month in the first quarter was used to create a functional relationship.Then,the distance between Spring Festival and the first day of every month in the first quarter of the target year was used as input and the predicted month-quarter ratio of electricity sales of each trade of every month in the first quarter of the target year was calculated according to the functional relationship.The adjusted electricity sales forecasting result can then be computed by using the predicted month-quarter ratio and the electricity sales forecasting of each trade of every month in the first quarter of the target year before adjustment.The adjustment result shows that the proposed method can effectively lower the error of electricity sales forecasting in the first quarter.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".