Evaluating the Feasibility of Reusing Pre-trained Thermal Models in the Residential Sector
Bibliographic record
Abstract
Modelling temperature dynamics of a building is necessary to develop control mechanisms for reducing energy consumption of heating and cooling equipment. While Resistance-Capacitance (RC) models can accurately explain how the indoor temperature changes over time, building such models requires the knowledge of the building insulation and thermal mass, which is not readily available for most residential buildings in operation today. In the absence of this information, model parameters can be estimated from coarsegrained data collected by smart thermostats. In this paper we train a Bayesian neural network to establish the RC model for a home equipped with a smart thermostat, and investigate how to reuse this model to predict the temperature inside another home which may not be equipped with a smart thermostat. Leveraging data from ecobee smart thermostats installed in over 4,000 homes in Canada, we validate that a small number of pre-trained neural network models is enough to develop a sufficiently accurate RC model for any home across the country and that this model outperforms a seasonal time-series model that is built using the same amount of data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".