An Event-Driven Neural Kalman Model for State Representation and Learning-Based Dynamic Scheduling of Industrial Energy System
Bibliographic record
Abstract
Operation optimization of industrial energy systems (IES) can effectively improve energy utilization efficiency and reduce carbon emissions. Learning-based optimization models such as reinforcement learning (RL) have been widely applied in dynamic scheduling of energy systems. However, they are usually time-driven periodic optimization that require the state variables having obvious time distribution rules, without considering the event-triggered characteristics of IES, and the uncertainty in state distribution caused by production-energy coupling. Thus, an event-driven neural Kalman state representation and dual-scale strategy learning framework is proposed in this study. To implement latent state construction with time distribution characteristics, an improved proximal policy optimization with neural Kalman state representation is developed to formulate time-driven scheduling policies, where the Kalman filter and RL parameters are trained in cascade to achieve an end-to-end learning process. In order to realize an event-driven policy learning, a state transition matrix connecting approach is proposed for collaborative calculation of time-event dual-scale policies. To verify the effectiveness of the proposed method, real data from a domestic steel company are employed for experiments. Commonly used and state-of-the-art approaches are selected for comparisons. The results validate the advantages of the proposed approach in terms of scheduling frequency, cost-effectiveness, and adaptation to industrial uncertain environments.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".