A Novel Intelligent Forecasting Framework for Quarterly or Monthly Energy Consumption
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
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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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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