Performance Study of a Multi-Objective Mathematical Programming Modeling Approach for Energy Optimization in Building Envelopes
Bibliographic record
Abstract
Architecture is very responsive to the environmental and climatic factors which affect the built environment with unsteady state. Technology can be used to keep these factors under control by optimizing building design to fit with the surrounding environment and the energy needs. In addition, building envelopes play a major role in achieving thermal comfort for occupants and reducing energy consumption. Building envelopes energy optimization became a leading approach in the architectural research and implementation. This study hypothesizes that using solar shading devices with suitable design decisions of the fenestration affects the daylight level, solar heat gain, visual comfort, and thermal comfort for users. The main objective of this study is to examine the relationships between building envelope design and building energy efficiency through research and simulations. In addition, it explores which combinations of the design elements are the most efficient in terms of lighting and HVAC loads which gives the designers a variety of optimum design solutions to choose the architectural from. This research focuses on the implementation and effectiveness of shading devices, glass type, and window-wall ratio (WWR) in energy optimization through building envelope. The study used MATLAB software for the mathematical model simulation while Revit software was used for the model validation. The study found that south orientation for buildings associated with horizontal shading elements is the best solution in terms of cooling loads in summer. In winter, however, the optimal solution was south orientation with vertical shading elements in terms of heating loads and illuminance levels.
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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 | 0.002 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".