A Multigenerational Solar Energy-Driven System for a Residential Building
Bibliographic record
Abstract
The residential sector represents a major source of increasing energy demands and greenhouse gas emissions. To meet these energy demands and reduce these harmful emissions, innovative solutions based on renewable energy sources must be developed. Here, a multigeneration solar energy-driven system that produces electric power, cooling, heating, and hot water to a residential building is introduced. Then, a thermodynamic model to analyze and evaluate this integrated system is developed by applying the four balance equations, namely mass, energy, entropy, and exergy. Next, a residential building in Ottawa, Ontario is selected as a base case for analyzing the performance of this integrated system. The results show that the integrated system has overall energy and exergy efficiencies of 39.2% and 19.9%, respectively. The highest source of exergy destruction rate is identified to be at the solar collector, so a careful design of this component is necessary. Next, parametric studies are conducted on this integrated system. The findings show that increasing the amount of solar energy received by the system increases its energy efficiency significantly, but not its exergy efficiency. Increasing the cooling load enhances the overall performance of the system and increases the hot-water production along with it due to the integration of different cycles in the multigeneration system.
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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) | 0.010 | 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".