An Optimal Switching Strategy for Operating <scp>CCHP</scp> Systems
Bibliographic record
Abstract
This chapter focuses on the optimization of the operation strategy for the existing combined cooling, heating, and power (CCHP) system. It elaborates following electric load (FEL) and following thermal load (FTL) for the CCHP system. The chapter explores the evaluation criteria (EC) function, which includes the primary energy consumption (PEC), carbon dioxide emissions (CDE), and operational cost (COST). It presents the EC-based optimal switching operation strategy. The chapter demonstrates the case studies based on a hypothetical CCHP system. In order to obtain an effective operation strategy, two primary steps are necessary: the construction of the performance criteria and the optimal design of the strategy. The proposed EC can also flexibly characterize the effects from policies and markets' variations by adjusting the weighting coefficients dynamically. In practice, the adjusting frequency of weighting coefficients and working frequency of the optimal strategy should be in different time scales.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".