Optimization Model for Sustainable Renovations \n in Buildings
Bibliographic record
Abstract
Buildings consume a substantial amount of energy and adversely affect the global climate and environment. According to the US Department of Energy (DOE), buildings account for 39% of total primary energy consumption and 71% of the electricity consumption. The construction and operation phases constitute the largest proportion of the total energy end-use worldwide (Ma et al. 2012). \n\t \nAn innovative and comprehensive set of sustainable materials aiming at the envelope of buildings excluding the roofs is employed to define the renovation alternatives in order to ameliorate the sustainability status of the buildings. The model is comprised of a NSGA-II multi-objective optimization algorithm integrated into a simulation engine. Simulation runs are performed to compute the objective function values and transfer them to the optimization algorithm. \n \nA hybrid fuzzy simulation-based optimization model is developed to select the optimum renovation alternatives. The model simultaneously minimizes annual energy consumption and capital cost of an existing office building based on a multi-objective optimization problem. Fuzzy set theory is assigned to the objective functions to address the uncertainty associated with calculation of energy consumption and capital cost values. Conclusively, the model is implemented on a sample case to substantiate the capabilities of the developed model. The case study is a one-story office building with a double skin facade on the south facing facade in Montreal. The results illustrate nine Pareto optimal points and demonstrate that the generated optimum solutions are capable of causing an average of 35% decrease in the annual energy consumption compared to the conventional building scenario.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".