Short Term Forecasts of Internal Temperature with Stable Accuracy in Smart Homes
Bibliographic record
Abstract
We forecast internal temperature in two homes, using variants of regression with data from the readings of multiple sensors. We use 48 separate models, where each forecasts mean temperatures that will occur in one future 15-minute interval, to compose a forecast for the next 12 hours. The sensors report internal and external atmospheric and environmental conditions such as temperature, pressure, sunlight, rain and wind, as well as evidence of human activity, including CO2 saturation, motion sensors and electrical load from areas within the house and large appliances. The models use both current and historical sensor values, each of which increases the number of predictors in the linear regression model. We use model simplification techniques including forward stepwise regression, principal component regression, and partial least squares regression. In both houses the forecast accuracy is stable; the mean absolute error over 12 hours is less than 1, while the root mean squared error is less than 1.3. Our accuracy compares favorably to previous work. Our work indicates long sensor histories for forecasts in the next 12 hours do not significantly improve accuracy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".