Climate resilience buildings: overheating detection in houses-investigating the feasibility of using smart thermostat data
Bibliographic record
Abstract
This report is the final deliverable of the project A1-017320.1. The main purpose of this report is to investigate the feasibility of using one of the emerging data sources, i.e. smart thermostats, along with machine learning approaches to detect and quantify overheating in buildings. This report is organized in two sections. Section A represents the results of the indoor overheating analysis in residential buildings based on smart thermostat data. In section B, a data-driven indoor conditions warning framework for residential buildings is developed and assessed. In section A, indoor temperature and humidity measurements from more than 3,000 connected thermostats, during summers of 2016-2019 and from homes located in five major metropolitan areas in Canada were used. Furthermore, a robust procedure to evaluate the frequency and severity of indoor overheating based on the heat-related health outcomes for older people was utilized. In particular, the overheating occurrence in homes with and without central air conditioning (AC) units was comparatively evaluated. The results of the first section showed that 12% of houses under study experienced at least one overheating event. Furthermore, the extreme indoor overheating events with potential health risks to the vulnerable occupants were more common amongst houses without central air conditioning units across the selected cities. In section B, a feasibility study was done on more than 180 houses located in Ontario that do not use central AC units. In this approach, two gray box models were developed based on the hourly indoor and outdoor temperatures. The gray box models were implemented in a recursive forecasting strategy using sliding training and forecast time periods to provide the forecasted hourly indoor temperatures. The proposed models were able to forecast 12-hour ahead hourly indoor conditions in 92% of houses with less than 5% mean absolute percentage error. This indicated the potential for leveraging data from IoT devices and machine learning, as part of an overheating detection and warning system in buildings. Although this work was based on data from residential buildings, the methodologies developed here can be used for other buildings.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".