Multi-objective Optimization of Integrated Community Energy and Harvesting (ICE-Harvest) System Based on Marginal Emission Factor
Bibliographic record
Abstract
In this study, a detailed optimization framework for optimal design and operation of a smart energy system so-called integrated community energy and harvesting (ICE-Harvest) system is devised. This system consists of a low-temperature single-pipeline network (LT-SPN), generation and storage units, and a set of buildings. Its key features are the capability to perform demand response without affecting the comfort of the occupants by manipulating the network temperature rather than the indoor temperatures. In addition, heating and cooling energy can be mutually beneficial, so that the energy that would otherwise be wasted is harvested. The resultant nonlinear optimization model is linearized, and a multi-criteria approach is utilized considering the total annual cost (TAC) and GHG emissions. The marginal emission factor (MEF) is calculated and used to evaluate the GHG emissions and showcase how dispatchable loads can reduce them. Finally, a Pareto front curve is obtained, the compromise solution is chosen, and its operation is discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".