Forecasting the total energy consumption in China using a new-structure grey system model
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
Purpose China is by far the world’s largest energy consumer and importer. Reasonably forecasting the trend of China’s total energy consumption (CTEC) is of great significance. The purpose of this paper is to propose a new-structure grey system model (NSGM (1, 1)) to forecast CTEC. Design/methodology/approach Two matrices for computing the parameters of NSGM (1, 1) were defined and the specific calculation formula was derived. Since the NSGM (1, 1) model increases the number of its background values, which improves the smoothness effect of the background value and weakens the effects of extreme values in the raw sequence on the model’s performance; hence it has better simulation and prediction performances than traditional grey models. Finally, NSGM (1, 1) was used to forecast China’s total energy consumption during 2016-2025. The forecast showed CTEC will grow rapidly in the next ten years. Findings Therefore, in order to meet the target of keeping CTEC under control at 4.8 billion tons of standard coal in 2020, Chinese government needs to take necessary measures such as transforming the economic development pattern and enhancing the energy utilization efficiency. Originality/value A new-structure grey forecasting model, NSGM (1, 1), is proposed in this paper, which improves the smoothness and weakens the effects of extreme values and has a better structure and performance than those of other grey models. The authors successfully employ the new model to simulate and forecast CTEC. The research findings could aid Chinese government in formulating energy policies and help energy exporters make rational energy yield plans.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 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 it