Multi-objective optimization of a residential building envelope in the Bahamas
Bibliographic record
Abstract
This paper uses a multi-objective optimization approach to assess building energy performance for residential homes in the Bahamas with the goal of providing objective data to policymakers to help achieve the country's sustainability goals. A non-sorting genetic algorithm (NSGA-II) is used to find optimal solutions to building design configurations such as wall types, insulation thickness, insulation type, etc. Building energy consumption and life cycle costs are the objectives. The study uses jEPlus+EA and EnergyPlus as simulation tools to perform the optimization to provide understanding of the interactions between the objectives and optimal design parameters. Optimal solutions obtained are compared with typical building designs to assess the performance of the optimal design configurations. The results indicate that the use of insulation in the envelope and improvements to the window fenestration are warranted. Also, the optimal solutions achieve energy reductions over current standards are up to 39% with a reduction in the life-cycle cost of up to 18.5%.
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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".