Wireless Sensor Networks for smart grid applications
Bibliographic record
Abstract
Electrical power grid is among the critical infrastructures of a nation. In the past several years, the power grids have experienced several major failures which have caused large financial losses in various countries around the globe. In a close future, the imbalance between the growing demand and the diminishing fossil fuels, aging equipments, and lack of communications are anticipated to negatively impact the operation of the power grids. For this reason, governments and utilities have recently started working on renovating the power grid to meet the power quality and power availability demands of the 21stcentury. The opportunities that have become available with the advances in Information and Communications Technology (ICT) have paved the way to this modernization. The new grid empowered by ICT is called as the smart grid. The natural extension of the smart grid applications to the consumer premises can be through Wireless Sensor Networks (WSNs) which are able to provide pervasive communications and control capabilities at low cost. WSNs have broad range of applications in the smart grid. In this paper we discuss the application of the WSNs in the home energy management services. We evaluate the performance of WSNs in terms of delivery ratio, delay and Packet Delay Variance (PDV) for varying interarrival times and varying network sizes. We also provide numerical results on the reduced cost, load and carbon emissions by our home energy management application.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".