The Urban Building Energy Retrofitting Tool: An Open-Source Framework to Help Foster Building Retrofitting Using a Life Cycle Costing Perspective—First Results for Montréal
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Building decarbonization is a major challenge for cities. Deciding which buildings to retrofit buildings, and when and how, is difficult, given the complex interaction between energy costs and investment requirements. Several tools have been developed in recent years to help public and private stakeholders with these decisions, but none cover aspects the authors think are fundamental. For this reason, an urban buildings retrofit tool was developed and is presented in this article. This new tool is based on a bottom-up approach, with all buildings simulated individually, considering aspects such as shading and adjacencies. As a second step, three scenarios with different levels of ambition were implemented in the tool, and the energy demand and emissions resulting from these scenarios were calculated. As a third step, the retrofitting scenarios’ initial investment and operational costs were implemented using a detailed Life Cycle Costing (LCC) approach. A robust and scalable structure was developed and applied to calculate the LCC of various retrofitting scenarios in Montréal, which will be described in detail.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it