Wireless Sensor Networks for domestic energy management in smart grids
Bibliographic record
Abstract
Wireless Sensor Networks (WSN) are getting more integrated to our daily lives and smart surroundings as they are being used for health, comfort and safety applications. In smart homes and office environments, WSNs are generally used to increase the inhabitant comfort. As the current energy grid is evolving into a smart grid, where consumers can directly reach and control their consumption, WSNs can take part in domestic energy management systems, as well. In this paper, we propose the Appliance Coordination (ACORD) scheme, that uses the in-home WSN and reduces the cost of energy consumption. The cost of energy increases at peak hours, hence reducing the peak demand is a major concern for utility companies. The ACORD scheme, aims to shift consumer demands to off-peak hours. Appliances use the readily available in-home WSN to deliver consumer requests to the Energy Management Unit (EMU). EMU schedules consumer requests with the goal of reducing the energy bill. We show that ACORD decreases the cost of electricity usage of home appliances significantly.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".