Application of a Modified Grey Model Based on Least Squares in Energy Prediction
Bibliographic record
Abstract
The GM (1,1) model is a prediction method based on the grey system theory, which can be used to handle the prediction problem of time-series data. Compared with the traditional time series model, GM (1,1) model has the characteristics of a simple model, small calculation amount and small samples, so it has a wide application prospect in practical application. Grey GM (1,1) model is a commonly used prediction model in the energy industry, which can effectively deal with small amounts of data and incomplete data, as an accurate, reliable and efficient prediction model to predict energy consumption. In this paper, based on the classical grey GM (1,1) model, the constant free term is introduced, the modified grey GM (1,1) model is proposed, and the least squares method is used to construct an optimization problem related to the model parameters, and finally solve the general expression of the constant free term. Finally, the model is used to predict more accurately the per capita electricity consumption (kilowatt-hours). The results show that the improved GM (1,1) model is better than the traditional GM(1,1) model, which verifies the effectiveness and practicability of the improved model and is suitable for the forecast of per capita electricity consumption.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".