Investigation of Energy and Environmental Potentials of a Renewable Trigeneration System in a Residential Application
Bibliographic record
Abstract
Micro polygeneration utilizing renewable energy is a suitable approach to reduce energy consumption and carbon emission by offering high-efficiency performance, offsetting the need for centrally-generated grid electricity and avoiding transmission/distribution losses associated with it. This paper investigates the energy and environmental potential of a renewable trigeneration system in a residential application under Incheon (Korea) and Ottawa (Canada) weather conditions. The trigeneration system consists of a ground-to-air heat exchanger (GAHX), photovoltaic thermal (PVT) panels and an air-to-water heat pump (AWHP). The study is performed by simulations in TRNSYS (Version 17.02) environment. The performance of the trigeneration system is compared to a reference conventional system that utilizes a boiler for space and domestic hot water heating and a chiller for space cooling. Simulation results showed substantial annual primary energy savings from the renewable trigeneration system in comparison to the reference system—45% for Incheon and 42% for Ottawa. The CO2eq emission reduction from the renewable trigeneration system is also significant, standing at 43% for Incheon and 82% for Ottawa. Furthermore, trigeneration systems’ capability to generate electricity and thermal energy at the point of use is considered as an attractive option for inclusion in the future smart energy network applications.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".