Simulation-Based Optimization of Building Renovation Considering Energy Consumption and Life-Cycle Assessment
Bibliographic record
Abstract
Buildings consume a tremendous amount of the total use of secondary energy resulting in a considerable impact on the environment. Therefore, it is necessary to reduce their energy consumption by improving the design of new buildings or renovating existing buildings. However, renovating building envelopes and energy systems to lessen energy losses is usually expensive and has a long payback period. Despite the significant contribution of the research about optimizing energy consumption, there is limited research focusing on the renovation of existing buildings to minimize their environmental impact using life cycle assessment (LCA). This study aims to develop a methodology to optimize the energy performance of existing buildings by selecting the optimal renovation strategies considering LCA. Different scenarios can be combined in a building renovation strategy to improve energy efficiency. Each scenario considers several factors including improvement of the building envelopes, heating, ventilation, and air conditioning (HVAC) systems, and local energy generation systems. However, some of these scenarios could be inconsistent and should be eliminated. Another consideration in this research is the appropriate coupling of renovation scenarios. For example, the HVAC system must be redesigned when renovating the building envelope to consider the reduced energy demand and to avoid undesirable side effects. A genetic algorithm (GA) coupled with an energy simulation tool is used for simultaneously minimizing the energy consumption and the LCA of the building. The simulation tool is used to calculate the energy consumption for each potential solution representing one renovation scenario. The data of the building characteristics are extracted from the building information model (BIM). The feasibility of the proposed method is demonstrated using a case study focusing on the buildings of Concordia University.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".