Lecture Notes in Computer ScienceSmart Multimedia A Real-Time Adaptive Thermal Comfort Model for Sustainable Energy in Interactive Smart Homes: Part I
Bibliographic record
Abstract
A successful smart city implementation needs to efficiently use natural and human resources. This can be achieved by dividing the smart city into smaller modules, such as a smart community, and even smaller such as a smart home, to allow energy management systems to monitor the city's behavior. The electricity end-user sector is often divided into the residential, commercial, and public transport, industrial, and agricultural sectors. On the other hand, HVAC systems constitute from 40% up to 60% of energy consumption in buildings. Nevertheless, householders do not entirely accept connected devices due to complex interfaces, lack of interest, or acquired habits of thermostat usage that affect thermal comfort, hence, usability and behavioral problems. Thermal comfort is widely defined as that state of mind which conveys satisfaction with the thermal surroundings. This paper obtains an adaptive comfort model for measuring these three features through energy simulations to compare them during the year. This paper analyzes three energy model scenarios to review the adaptive behavior of a community of twelve houses. Three energy models were simulated for Mexico City, Concord (California), and Ontario (Canada) and later deployed into an interactive online platform to determine what further actions are required to improve the quality of life of householders without losing thermal comfort and maximizing energy savings. Besides, this platform allows worldwide users to interact with the platform and learn how clothing insulation, activity, and location affect energy consumption and thermal comfort.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.097 | 0.036 |
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".