An Industrial Energy Prediction Method Integrating Planning Information and Process Correlation Characteristics
Bibliographic record
Abstract
An accurate prediction of energy production and consumption is a prerequisite for realizing reasonable energy scheduling in process industry. However, under the conditions of production-energy coupling, the energy operating status is highly related to production rhythm. Many prediction methods are unable to consider the impact of multi-production process correlation and planning constraints on energy data fluctuations. To tackle this problem, an industrial energy prediction method integrating plan and multi-dimensional data correlation is proposed. A data augmentation method based on wavelet matching is developed to extract specific features of the energy data and obtain augmented samples. To capture the alternating operation characteristics of different production processes, a contrastive learning (CL) method with probability jumping is developed that takes the process uncertainty into consideration. On this basis, the planning information is represented by a novel form of partial differential equations (PDEs), so that the global production information can be embedded as a priori knowledge within a physics-informed neural network (PINN) to achieve dynamic energy prediction. In order to validate the effectiveness of the proposed method, experiments are conducted using energy data from a steel company and compared with a variety of state-of-the-art methods. The results verify that the proposed method achieves better prediction results in complex industrial scenarios containing process coupling and planning constraints.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".