Factorial Based Stochastic Optimization Approach for Energy and Environmental Systems Management Under Uncertainty
Bibliographic record
Abstract
In this study, an optimization programming based on inexact stochastic method and factorial design was proposed to support management of energy and environmental systems under uncertain conditions. It could be used for analyzing various policy scenarios associated with different levels of economic penalties when promised targets are violated. Moreover, it can obtain optimal decisions of primary energy supply, electricity, and thermal power generation, capacity expansion, and emission control scheme. The developed model has been applied to a case study within a multifacility, multiperiod, and multidemand-level context to demonstrate the feasibility of the proposed methodology. Factorial method has been used for sensitivity analysis to address the interactive uncertainties in modeling parameters, as well as providing a trade-off analysis between the economic objective and the relevant energy and environmental policies. The generated approach will be able to reflect dynamic complexities in energy and environmental systems under social–economic–environmental requirements. It is helpful for adjusting the interrelationship among conflicting economic objectives and environmental benefits under multiple uncertainties, formulating allocation patterns of energy resources and services, and identifying the effectiveness of current regulations.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".