A Novel Intelligent Forecasting Framework for Quarterly or Monthly Energy Consumption
Bibliographic record
Abstract
Accurately predicting quarterly or monthly energy consumption remains challenging so far. Despite the abundance of relevant studies, most of them focus on univariate modeling. Moreover, the core of nearly all multivariate forecasting studies is an unstable forecasting system based on a single model. Therefore, there is an urgent need for an efficient and rational prediction method. For the prediction task of quarterly or monthly energy consumption characterized by small samples and nonlinearity, this article develops a new joint forecasting-centered forecasting framework by integrating machine learning and grey system theory. In this forecasting framework, grey relational analysis is used to filter the influencing factors of the study object, a new adaptive weighted least squares support vector regression model is developed to describe the relationship between the study object and the filtered influencing factors, and a new difference equation prediction model is employed to predict the future values of the filtered influencing factors. The joint forecasting task is accomplished by inputting the future values of the filtered influencing factors into the trained adaptive weighted least squares support vector regression model. Experimental simulation results demonstrate that the two prediction models developed in this framework, along with the overall forecasting approach, outperform competing methods. These results confirm the effectiveness of the proposed forecasting framework in accurately predicting quarterly or monthly energy consumption, even in scenarios with limited data and nonlinear relationships.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".