HVAC characterisation of existing Canadian buildings for decarbonisation retrofit identification
Bibliographic record
Abstract
Building archetypes are useful in building energy simulations as they simplify the modelling process. These building archetypes are classified in the Building Technology Assessment Platform (BTAP), a database built on Natural Resources Canada building codes. There are two groups: buildings established 1980 to 2004 and buildings established before 1980. The major drawback with the BTAP archetypes is that there are no considerations made regarding changes in mechanical systems in pre-1980 buildings, nor are the impacts of this evolution examined. This study expands the available archetypes by investigating typical heating, ventilation and air conditioning (HVAC) systems used for offices and multi-unit residential buildings in the City of Toronto by analysing data from municipal and industry partner sources to determine system characteristics for each building type for each period and suggest retrofits for the selected characteristics. This study identifies common building clusters based on building topology, size and vintage to develop more varied archetypes. By increasing the granularity of existing archetypes and presenting them for ASHRAE climate zone 5 A, retrofit modelling for Canadian buildings will improve in accuracy. Both baseline and retrofit conditions are modelled in both current and decarbonised thermal and electricity source conditions to understand the relative benefit of individual building vs district utility retrofits. Practice relevance This study furthers the applications of archetype development in North America by developing a set of granular HVAC system characterisations to better model existing buildings. This will support urban- and portfolio-scale energy modelling by enabling rapid simulation of existing buildings with increased accuracy versus existing ‘reference model’ methods.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".