Smart hybrid renewable microgeneration system for residential applications
Bibliographic record
Abstract
Microgeneration systems generate power and heat at the point of use by utilising a variety of conventional and renewable technologies. They demonstrate a comparable electric efficiency to the conventional power generation stations, good environmental performance and ability to serve as a source for both primary and back-up power. Assembled in microgrids or in 'virtual power plant' they can serve multiple buildings and be active participants in load management efforts both on site and on the grid. The study investigates the performance of a hybrid renewable ground source heat pump (GSHP)/photovoltaic thermal (PVT) microgeneration system serving multiple residential and small office buildings in Ottawa, Canada and Incheon, South Korea. The analysis shows that the energy performance of the GSHP/PVT system results in considerable overall energy savings in comparison to conventional and single GSHP system due to the higher renewable component. The energy analysis results indicate that the extra capital investment incurred to the GSHP-PVT system is possible to be returned within its lifespan, especially with the current trend of continuous equipment and installation price reductions. Further reducing of buildings' dependence from the electricity grid could also be achieved within the 'smart energy networks' concept and with utilities various load shaving and load levelling strategies.
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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 | 0.000 |
| 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) | 0.007 | 0.002 |
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".