A comparison of 4th and 5th generation thermal networks with energy hub
Bibliographic record
Abstract
State-of-the-art thermal networks are key to address decarbonization of the heating and cooling in buildings. Energy Hub considers synergy between elements and allows rapid comparison between district energy systems incorporating 4th generation (4G) and 5th generation (5G) thermal networks at early-stage designs. To better understand and quantify the differences between systems based on 4G and 5G, a mixed-integer linear model distinguishing specific features of both technologies is implemented. We find systems relying on 5G to perform environmentally and financially better than with 4G generation over a wide set of scenarios. This is due to the warm/cold coupling characterizing 5G technologies. Systems based on 5G can also reduce its carbon emissions more than those with 4G. However, performances with both technologies appear sensitive to the topology and location of central energy station. • MILP model for district energy system relying on 4G and 5G thermal networks. • Comparison of design, financial and environmental performances. • Systems using 5G show a higher efficiency due to the warm/cold coupling.
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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".