Investigating the thermal performance of canadian houses using smart thermostat data
Bibliographic record
Abstract
Evaluating the thermal performance of the building stock using traditional methods (e.g., energy audits) can be cost prohibitive, especially if it aims to cover hundreds or thousands of buildings. This paper presents a novel data-driven approach to investigate the thermal performance of existing buildings using data collected from smart thermostats. The study focused on more than 11,000 Canadian homes and relied on real-time indoor and outdoor temperature measurements at 5-minute intervals over a period of four years. Two grey-box modelling approaches namely, least-squares fitting of 1) decay curves, and 2) numerically integrated thermal energy balance equations were used to estimate a thermal time constant for each house. It represented the time it takes for a house to achieve a new thermal equilibrium in response to changes in its internal and external thermal conditions. The resulting time constant values from both models were analyzed with respect to ASHRAE climate zones, building-age, building-style, and floor-area. Results indicated that building age was the most influential factor on these values, irrespective of the modelling approach. Given the correlation between building age and changes in Canadian building code provisions, the effect of code changes on these buildings’ thermal performance was also investigated. By using a large sample size covering thousands of buildings nationwide, results of this research are beneficial for code officials and policymakers to confirm the effect of previous code changes on the building stock and can inform the development of new energy codes and retrofit programs.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".