Transformer-based predictive energy management in hydrogen-integrated renewable systems
Bibliographic record
Abstract
Hydrogen, once produced from renewable energy via water electrolysis, can power zero-carbon transportation with the additional benefits of reducing combustion-generated air pollutants including particulates and greenhouse gases. Meanwhile, storage of hydrogen, in either its gaseous or liquid form, does not require rare-earth materials and hence it becomes more economic and environmentally friendly. Incorporating hydrogen and battery storage technologies into an advanced predictive energy management approach to electrolysis based systems can significantly enhance renewable energy utilization, optimize power use from grid, improve system adaptability, and increase overall energy efficiency. This study develops a dynamic energy management algorithm by employing transformer-based time series models for accurate demand forecasting, which enables rolling window optimization using model predictive control. Simulation results indicate that this approach improves demand forecasting accuracy by 41.21% and increases the adjusted green hydrogen production rate from 29.54% to 54.3% compared to the conventional model. It is further demonstrated that the renewable-based hybrid energy storage system can achieve a renewable energy production ratio of 71% with an effective 95% renewable energy utilization rate, while the grid dependency is reduced from 57% to 43%. This study proposes a novel dynamic energy management approach in a hydrogen-integrated Renewable Energy System, using transformer-based models for time series forecasting and a model predictive control framework for rolling window optimization. • Preformed transformer-based rolling window optimization for dynamic energy management • Developed a predictive model for hydrogen-integrated renewable energy systems • Revealed the strategy to improve green hydrogen production while reducing the grid dependency
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".