Ultra-Low Carbon Technologies for Building Retrofits
Bibliographic record
Abstract
Reaching the goal of net-zero carbon consumption is particularly challenging for existing buildings. Calibrated energy simulations, using IES VE and EnergyPlus, of a portfolio of ten existing commercial buildings in Canada were used to develop paths to net zero carbon. Emerging technologies including carbon sequestration, algae farming, electrochromic glass, predictive controls, building integrated photovoltaic, and fuel cell technologies were evaluated for their applicability in today’s built environment. This paper provides recommendations and commentary on the available information, relative impact and economic feasibility of innovative and new but proven technologies for evaluation in existing building retrofit simulations. This research paper is highly relevant to building owners, and simulation specialists given that in 2030, 75% of our building stock will consist of buildings in existence today (ECCC, 2016), while net zero carbon strategies typically focus on new construction methodologies. Expertise in recommending and analysing improvement measures for existing buildings represents an exciting and growing opportunity for building simulation specialists. This paper provides detailed and tested recommendations on the relative utility of emerging technologies for achieving net-zero carbon and provides comment on the relative percentage of retrofit budget that was diverted to innovative and emerging technologies to simulate maximum potential site carbon reduction.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".