Strategic Retrofit Investment from the Portfolio to the Building Scale: A Framework for Identification and Evaluation of Potential Retrofits
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
Buildings account for approximately 40% of the total energy consumption and associated GHG emissions globally. Within this sector, approximately 80% of buildings in North America are more than 15 years old and in the intervening years, energy codes have been revised to require 25% less energy than buildings meeting 2000 codes. As a result, even sustainably-rated buildings over 10 years old tend to compare poorly with new buildings built to minimum code standards. Significant investment is required to bring the performance of these buildings in line with market expectations and competition for newer, more sustainable buildings. In large portfolios, the challenge is not only to identify the optimal building retrofits, but also which buildings have the most improvement potential. This paper presents a three step approach to overcome this challenge. First, the building portfolio is screened from the often limited available data and potential energy improvement potential and commercial improvement potential are ranked to identify priority buildings. Second, a series of retrofit bundles is tested on the priority buildings to calculate estimated energy savings, required capital and resultant operating costs and qualitative indicators affecting occupant comfort to prepare a financial analysis. These estimates are refined using an energy model for the most promising retrofit bundle. Finally, portfolio - wide strategies are developed for the non-priority buildings to take advantage of “low hanging fruit”. Four global case studies using this framework are presented, two at the portfolio scale and two at the building scale. In each case, the evaluation tools and techniques, modeling approaches and data used in this decision-making are described and resulting project recommendations are presented. Although developed primarily for commercial buildings, this approach is applicable to all building types and recommendations are offed for its adaptation across sectors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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