Optimizing the selection of sustainability measures to minimize life-cycle cost of existing buildings
Bibliographic record
Abstract
Buildings have significant impacts on the environment and economy as they were reported by the World Business Council for Sustainable Development in 2009 to account for 40% of the global energy consumption. Building owners are increasingly seeking to integrate sustainability and green measures in their buildings to minimize energy and water consumption as well as life-cycle cost. Due to the large number of feasiblecombinations of sustainability measures, decision makers are often faced with a challenging task that requires them to identify an optimal set of upgrade measures to minimize the building life-cycle cost. This paper presents a model for optimizing the selection of building upgrade measures to minimize the life-cycle cost of existing buildings while complying with owner-specified requirements for building operational performance and budget constraints. The optimization model accounts for initial upgrade cost, operational cost and saving, escalation in utility costs, maintenance cost, replacement cost, and salvage value of building fixtures and equipment, and renewable energy systems. A case study of a rest area building in the state of Illinois in the United States was analyzed to illustrate the unique capabilities of the developed optimization model. The main findings of this analysis illustrate the capabilities of the model in identifying optimal building upgrade measures to achieve the highest savings of building life-cycle cost within a user-specified upgrade budget; and generating practical and detailed recommendations on replacing building fixtures and equipment and installing renewable energy systems.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".