A Study of Building Thermal Dynamics from Large Data Sets: An Application for Residential Smart Thermostats
Bibliographic record
Abstract
This thesis focuses on identifying the Thermal Time Constant (TTC), a thermal performance indicator related the building’s effective thermal insulation, airtightness, and thermal storage capacity. Using data from over 15,000 smart thermostats, data mining is applied to identify patterns in the short-term transient thermal response of Canadian and American dwellings. The data used consist of contextual information (i.e. metadata) and one year of measurements recorded at 5-minute intervals (i.e. indoor air temperature, outdoor air temperature, and Heating, Ventilation and Air-Conditioning (HVAC) equipment run times). The TTC is captured from the data by tracking the indoor temperature response of the free-running dwelling over a specific time period, and by also assuming this response can be accurately described by the characteristic exponential decay (or growth) of a first-order resistance-capacitance thermal model. Consequently, the results show significant differences between estimated TTC values for the summer and winter months across ASHRAE climate zones 1 through 7. In winter, the mean TTC related to these climate zones ranges from 7 to 47 hours. In contrast, the summer mean values vary between a lower and narrower range of 6 to 19 hours which can presumably be attributed to occupants opening the windows, and thus effectively reducing their dwelling’s overall thermal resistance. Towards the larger objectives of thermal resilience, energy savings and grid reliability, the estimated TTC values can be used in the residential sector to quickly identify buildings eligible for building enclosure retrofits or to rapidly generate a simple model to inform thermal load estimation and management.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".