Multi-objective optimization framework for the selection of configuration and equipment sizing of solar thermal combisystems
Bibliographic record
Abstract
Solar combisystems supplying thermal energy for both domestic hot water and space heating can reduce primary energy consumption for residential buildings. As their overall performance depends on their design, this paper presents the development and use of a multi-objective optimization framework for the selection of configuration and equipment sizing of a residential solar combisystem in Montreal, Quebec, Canada. A generic solar combisystem model, which enables different configurations to be chosen, is first developed, and then coupled with a micro-time variant multi-objective particle swarm optimization, which was developed and used to find the non-dominated combisystem design alternatives. The life cycle cost (LCC), energy use (LCE), and exergy destroyed (LCX) of the solar combisystem are used as objective functions to find the best feasible designs. For the minimum LCC, only one flat-plate collector is required to store energy within one thermal storage tank, whereas seven evacuated-tube collectors and two thermal storage tanks are used for the minimum LCE value. The micro-time variant multi-objective particle swarm optimization (micro-TVMOPSO) algorithm used found a non-dominated solution that reduced the LLC by 29% compared with the initial design solution, with the increase of LCE by 72%. Since the LCC and LCE objective functions are conflicting, another non-dominated solution increased the LCC by 36% and reduced the LCE by 27%. The proposed multi-objective optimization framework can therefore be used to get the most out of solar thermal combisystems given specific economic and environmental conditions.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".