Learning from failure: understanding the anticipated–achieved building energy performance gap
Bibliographic record
Abstract
Over the past 20 years a number of studies have identified and provided explanations for a significant ‘performance gap' between designed and actual energy performance of buildings. The anticipated and achieved energy performance of an advanced, innovative building that aspired to net-positive energy performance is studied: the Centre for Interactive Research on Sustainability (CIRS) building at the University of British Columbia in Vancouver, Canada. Selected performance ‘failures’ that became evident during operation of CIRS are studied for how they were discovered and the efforts required for their resolution: the energy systems and associated controls and monitoring. The key findings show the barriers were neither economic nor technical. Instead, the primary impediments were institutional regimes – arising from the ways that various life-cycle stages were specified, contracted and implemented. The key issues emphasize the importance of having meaningful and effective building energy monitoring capabilities, an understanding of energy system boundaries in design and analysis, crossing the gaps between different stages of a building life cycle, and feedback processes throughout design and operation. The disclosure of ‘failure’ and lessons learned is a valuable contribution to subsequent advancement for the building stakeholders and the wider professional and research communities.
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.
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 | 0.028 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.014 | 0.030 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".